Artificial Intelligence-Assisted Pharmaceutical Manufacturing Optimization Using Predictive Machine Learning Techniques
Main Article Content
Abstract
The pharmaceutical manufacturing sector is seeing growing difficulties in streamlining operations, preserving product quality, and meeting stringent regulatory standards. Now more than ever, AI and ML are poised to revolutionize pharmaceutical production by evaluating data and generating predictions to optimize various elements. Utilizing a combination of AI-driven approaches, massive volumes of industrial data may be employed for process monitoring, quality prediction, defect detection, and production optimization. Using machine learning techniques like the XGBoost algorithm in conjunction with the SECOM production Process Dataset from UCI, this research suggests a smart framework for optimizing pharmaceutical production. Here are some potential approaches for data cleaning: Data balancing, feature scaling, dimensionality reduction, and missing data imputation are the four pillars of ADASYN, a methodology that improves the precision and effectiveness of data models. Experimental results show that the suggested XGBoost model achieves better results than competing models in terms of ACC (98.93%), PRE (98.97%), REC (98.99%), and F1 score (98.92%). These models include SVM, RF, DT, DNN, and MLP. Improvements in quality control, process efficiency, and intelligent decision-making are achieved via the use of the AI-based framework, which is proven to be a reliable way for enhancing predictive pharmaceutical manufacturing.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain the copyright of their work and grant the Journal of Global Research in Multidisciplinary Studies (JGRMS) the right of first publication. This license permits unrestricted use, distribution, adaptation, and reproduction in any medium or format, provided the original author(s), source, and publication are properly credited. Users may copy, redistribute, remix, transform, and build upon the published material for any purpose, including commercial use, in accordance with the terms of the CC BY 4.0 License.
How to Cite
References
[1] S. Mahmud, K. K. Mohammed, V. R. Jain, and S. A. Shah, “Artificial Intelligence-Driven Predictive Models for Identifying Risk Factors of Chronic Diseases,” in 2026 14th International Symposium on Digital Forensics and Security (ISDFS), Boston, MA, USA: IEEE, Mar. 2026, pp. 01–06. doi: 10.1109/ISDFS69419.2026.11459003.
[2] A. Warrier and A. K S, “Autonomous Agentic AI for Clinical Workflow Orchestration: Self-Managing Healthcare Operations,” in 2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), IEEE, Dec. 2025, pp. 1450–1457. doi: 10.1109/ICICNIS66685.2025.11315712.
[3] R. Kant, R. Rao Thallada, B. Pandey, and P. Srivastava, “AI-Based Cybersecurity in Healthcare: A Data-Driven, Governance-Aware Framework for Secure Clinical Systems,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 2026, pp. 1–5. doi: 10.1109/ICAIC67076.2026.11395836.
[4] N. N. Agarwal et al., “Technological Forecasting & Social Change E-health and wellbeing monitoring using smart healthcare devices : An empirical investigation,” Res. Policy, 2020.
[5] R. Snehamrutha, “Patient Engagement Strategies in Community Pharmacies and their Effect on Vaccination Uptake and Medication Synchronizations,” ESP J. Eng. Technol. Adv., vol. 3, no. 3, pp. 163–173, 2023, doi: 10.56472/25832646/JETA-V3I7P120.
[6] S. Kukar, “Explainable AI Models for Transparent and Ethical Customer Relationship Management Decision,” in 2026 IEEE 2nd International Conference on Secure IoT, Assured and Trusted Computing (SATC), Houston, TX, USA: IEEE, Mar. 2026, pp. 1–7. doi: 10.1109/SATC69565.2026.11542554.
[7] S. Mukherjee, “AI-Driven Deep Learning System for Alzheimer’s Disease (AD) Detection Via MRI Imaging,” in 2026 International Conference on Intelligent Computing, Networks, and Security (IC-ICNS), Bhubaneswar, India: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IC-ICNS68863.2026.11537765.
[8] V. K. Sharma, Z. Khan, R. K. Sharma, and D. Jindal, “Artificial Formulation Development in Pharmaceutics : Current Applications , Challenges and Future Perspectives,” vol. 4, no. 7, pp. 193–198, 2026, doi: 10.5281/zenodo.21113149.
[9] S. R. Katta, “Optimizing Pharmaceutical Manufacturing through AI-Powered Calibration and Maintenance Workflow Management,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 4, pp. 2175–2181, Nov. 2023, doi: 10.51219/JAIMLD/Srikanth-reddy-katta/476.
[10] O. Afzal et al., “Nanoparticles in Drug Delivery: From History to Therapeutic Applications,” Nanomaterials, vol. 12, no. 24, p. 4494, Dec. 2022, doi: 10.3390/nano12244494.
[11] Y. Jia et al., “Approved Nanomedicine against Diseases,” Pharmaceutics, vol. 15, no. 3, p. 774, Feb. 2023, doi: 10.3390/pharmaceutics15030774.
[12] R. Lingam, “Agentic AI and Federated Learning for Privacy-Preserving Dental Imaging,” in 2026 Innovations in Machine, Engineering, and Digital Conference (IMED), Kota Kinabalu, Malaysia: IEEE, 2026, pp. 1–6, March. doi: 10.1109/IMED68921.2026.11484887.
[13] A. Warrier, “Real-Time AI Integration Architectures for HIPAA-Compliant Healthcare Data Interoperability,” in International Journal of Emerging Trends in Computer Science and Information Technology, Eureka Vision Publication, 2025, pp. 74–81. doi: 10.63282/3050-9246/WCAI25-128.
[14] Noorain, V. Srivastava, B. Parveen, and R. Parveen, “Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects,” Curr. Drug Metab., vol. 24, no. 9, pp. 622–634, Sep. 2023, doi: 10.2174/0113892002265786230921062205.
[15] S. Kolluri, J. Lin, R. Liu, Y. Zhang, and W. Zhang, “Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review,” AAPS J., vol. 24, no. 1, p. 19, Jan. 2022, doi: 10.1208/s12248-021-00644-3.
[16] P. Suriyaamporn, B. Pamornpathomkul, P. Patrojanasophon, T. Ngawhirunpat, T. Rojanarata, and P. Opanasopit, “The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review,” AAPS PharmSciTech, vol. 25, no. 6, p. 188, Aug. 2024, doi: 10.1208/s12249-024-02901-y.
[17] W. Wang, Z. Ye, H. Gao, and D. Ouyang, “Computational pharmaceutics - A new paradigm of drug delivery,” J. Control. Release, vol. 338, pp. 119–136, Oct. 2021, doi: 10.1016/j.jconrel.2021.08.030.
[18] P. Bannigan, M. Aldeghi, Z. Bao, F. Häse, A. Aspuru-Guzik, and C. Allen, “Machine learning directed drug formulation development,” Adv. Drug Deliv. Rev., vol. 175, p. 113806, Aug. 2021, doi: 10.1016/j.addr.2021.05.016.
[19] S. A. Devanira Poovaiah, M. Sankaran, G. Namazov, N. Jooluri, and M. Mukhlisa, “Alzheimer’s Disease Detection from MRI Scans using Deep Hybrid Architecture with Metaheuristic Hyperparameter Tuning,” in 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), IEEE, Jul. 2025, pp. 1–7. doi: 10.1109/ISAC364032.2025.11156608.
[20] J. Kehrein, A. Bunker, and R. Luxenhofer, “POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles,” Mol. Pharm., vol. 21, no. 7, pp. 3356–3374, Jul. 2024, doi: 10.1021/acs.molpharmaceut.4c00086.
[21] N. Mozafari, N. Mozafari, A. Dehshahri, and A. Azadi, “Knowledge Gaps in Generating Cell-Based Drug Delivery Systems and a Possible Meeting with Artificial Intelligence,” Mol. Pharm., vol. 20, no. 8, pp. 3757–3778, Aug. 2023, doi: 10.1021/acs.molpharmaceut.3c00162.
[22] K. Thupphae and N. Wiwatwattana, “Data-Driven Application and Comparison of Machine Learning Models for Drug Dissolution Prediction in Pharmaceutical Quality Risk Management,” in 2026 23rd International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, Jun. 2026, pp. 144–149. doi: 10.1109/JCSSE68839.2026.11596766.
[23] R. Mannam, “A Machine Learning Framework to Predict QC Lab OOS Events and Reduce Rework,” in 2026 5th International Conference on Computer Networks, Big Data and IoT (ICCBI), IEEE, Jun. 2026, pp. 558–564. doi: 10.1109/ICCBI68589.2026.11619806.
[24] R. M, R. Patil, D. G. Sarode, and S. A. Sakharwade, “AI-based Smart Control Systems for Induction Brazing in Industrial Applications,” in 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), IEEE, Apr. 2025, pp. 1530–1537. doi: 10.1109/ICTMIM65579.2025.10988032.
[25] M. Bhende, A. Salati, P. S. Kumar, P. Senthil, S. M, and G. Ali, “The Role of AI and Robotics in Fully Autonomous Pharmaceutical Manufacturing Facilities,” in 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT), IEEE, Sep. 2025, pp. 1–7. doi: 10.1109/ICONAT66879.2025.11362620.
[26] F. Kakavandi, P. Han, T. B. Nerenst, R. de Reus, C. Gomes, and P. G. Larsen, “Comparative Study in Fault Detection under Data Scarcity for Pharmaceutical Device Manufacturing,” in 2025 International Conference on Control, Automation and Diagnosis (ICCAD), IEEE, Jul. 2025, pp. 1–6. doi: 10.1109/ICCAD64771.2025.11099417.
[27] P. Malathi, S. Jansi, M. V. Prabhakaran, K. Senbagam, M. P. Sujatha, and R. Sadaieswaran, “Big Data Analytics for Quality Control in The Pharmaceutical Industry,” in 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2024, pp. 669–674. doi: 10.1109/ICACCS60874.2024.10717314.
[28] L. D. Lam, B. P. Le Luong, H. T. Mai Linh, and P. M. Hung, “Application of Machine Learning in Predicting the Amount of Pharmaceutical Drugs Ordered for the Manufacturer,” in 2023 1st International Conference on Health Science and Technology (ICHST), IEEE, Dec. 2023, pp. 1–6. doi: 10.1109/ICHST59286.2023.10565367.
[29] A. Farrag, M.-K. Ghali, and Y. Jin, “Rare Class Prediction Model for Smart Industry in Semiconductor Manufacturing,” 2024.
[30] D. K. Nguyen, C. L. Chan, and D. Van Phan, “Gradient Deep Learning Boosting and Its Application on the Imbalanced Datasets Containing Noises in Manufacturing,” Smart Innov. Syst. Technol., vol. 314, pp. 225–235, 2023, doi: 10.1007/978-3-031-05491-4_23.
[31] H. J. Park, Y. S. Koo, H. Y. Yang, Y. S. Han, and C. S. Nam, “Study on Data Preprocessing for Machine Learning Based on Semiconductor Manufacturing Processes,” Sensors, vol. 24, no. 17, pp. 1–14, 2024, doi: 10.3390/s24175461.
[32] D. Moldovan, I. Anghel, T. Cioara, and I. Salomie, “Particle Swarm Optimization Based Deep Learning Ensemble for Manufacturing Processes,” in 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE, Sep. 2020, pp. 563–570. doi: 10.1109/ICCP51029.2020.9266269.
[33] R. Alharbi, “From synthetic data to actionable insights: fault detection in industry,” Complex Intell. Syst., vol. 12, no. 8, 2026, doi: 10.1007/s40747-026-02323-2.