Bioequivalence (BE) studies ensure the therapeutic equivalence of generic drugs to brand-name counterparts, traditionally relying on High-Performance Liquid Chromatography (HPLC). However, HPLC workflows face challenges such as matrix complexity, peak variability, and labour-intensive data processing. Artificial Intelligence (AI) offers transformative solutions, with machine learning (ML) models such as Radial Basis Function (RBF) networks, Multilayer Perceptrons (MLPs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs) enhancing chromatographic workflows. These models optimise retention time prediction, automate peak integration, and improve Quantitative Structure-Property Relationship (QSPR) and Quantitative Structure-Retention Relationship (QSRR) modelling for solvent selection. AI-driven approaches streamline BE studies, reducing experimental workload while improving accuracy and reproducibility. Despite its promise, AI integration poses challenges related to data bias, regulatory acceptance, and ethical concerns. With evolving regulatory guidelines from the FDA and EMA, AI is poised to redefine BE assessments, advancing drug development and ensuring more precise, cost-effective, and regulatory-compliant pharmaceutical analysis.