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.