Artificial Intelligence and Machine Learning in Pharmaceutical Analytical Chemistry: Transforming Drug Quality Control and Process Optimization. A Review
Authors
Article Information
Publication Timeline
- Published
Abstract
AI and ML in pharmaceutical analytical chemistry are transforming drug quality control and optimizing different aspects such as method, development, and manufacturing processes. This review aims to explore how AI and ML are changing four major UV-Vis spectrophotometry, High Performance Liquid Chromatography (HPLC), NIR spectroscopy, and Raman spectroscopy in pharmaceutical applications (2019-2025). It provides analytical solutions for quality control of the pharmaceutical industry and assesses the main ML algorithms: supervised models (ANN, SVM, RF, CNN), unsupervised methods (PCA), and hybrid chemometric-AI solutions. AI’s role in conjunction with Process Analytical Technology (PAT), real-time release testing (RTRT), continuous manufacturing, and evolving drug solubility estimation, dissolution, and impurity detection is also evaluated. Some regulatory and interpretability challenges (e.g. compliance with ICH Q2(R2), Explainable AI (XAI), and validation of models) are discussed with possible solutions. The review proves that AI and ML in analytical methods for predicting quality control issues in the pharmaceutical industry outperform classical chemometric and AI solutions in terms of predictability and speed. However, data quality, model repeatability, and regulatory concerns are the biggest challenges in implementing these methods in the pharmaceutical industry.
Keywords
Artificial intelligencePharmaceutical quality controlChemometricsProcess Analytical TechnologySpectroscopic methodsHow to Cite
Ahmad, F. H. A., & Al-Sarraj, T. Z. (2026). Artificial Intelligence and Machine Learning in Pharmaceutical Analytical Chemistry: Transforming Drug Quality Control and Process Optimization. A Review. International Journal of Medical and All Body Health Research, 7(2), 113-117. https://doi.org/10.54660/IJMBHR.2026.7.2.113-117
Ahmad, Fayhaa Hassan Ali, and Thura Z. Al-Sarraj. "Artificial Intelligence and Machine Learning in Pharmaceutical Analytical Chemistry: Transforming Drug Quality Control and Process Optimization. A Review." International Journal of Medical and All Body Health Research, vol. 7, no. 2, 2026, pp. 113-117. https://doi.org/10.54660/IJMBHR.2026.7.2.113-117.
Ahmad, F. H. A. and Al-Sarraj, T. Z. (2026) 'Artificial Intelligence and Machine Learning in Pharmaceutical Analytical Chemistry: Transforming Drug Quality Control and Process Optimization. A Review', International Journal of Medical and All Body Health Research, 7(2), pp. 113-117. doi: 10.54660/IJMBHR.2026.7.2.113-117.
Ahmad FHA, Al-Sarraj TZ. Artificial Intelligence and Machine Learning in Pharmaceutical Analytical Chemistry: Transforming Drug Quality Control and Process Optimization. A Review. International Journal of Medical and All Body Health Research. 2026;7(2):113-117. doi:10.54660/IJMBHR.2026.7.2.113-117
Similar Articles
- An Interpretable XGBoost Model for Diabetes Prediction: Nested Cross-Validation, Calibration, and SHAP Analysis Vol 7, Issue 3 (2026)
- Personalized Exercise Prescription in Neuro Physiotherapy: Current Evidence and Future Perspectives Vol 7, Issue 3 (2026)
- Explainable Artificial Intelligence and Predictive Analytics: Applications Across Healthcare, Finance, Cybersecurity, and Sustainable Systems Vol 7, Issue 3 (2026)
- Systematic and Integrative review on Artificial intelligence in the Diagnosis of Dementia, Brain Neuroplasticity, and Healthy Aging Vol 7, Issue 1 (2026)
- Machine Learning–Driven Clinical Decision Support Systems for Improving Patient Outcomes in US Healthcare Vol 4, Issue 4 (2023)
Find this Article on
Google Scholar Semantic Scholar OpenAlex Scilit Crossref ResearchGate BASEA newly published article may take a few days to appear on these services.