Artificial intelligence (AI) combined with computer-aided drug design (CADD) has emerged as a powerful approach to address challenges in drug discovery. This study applies AI-driven CADD techniques to identify novel aromatase inhibitors for hormone-dependent breast cancer using benzoxazole scaffolds. 3D-QSAR models were built using CoMFA and CoMSIA, validated by deep learning algorithms (artificial neural networks (ANN)). Supervised machine learning algorithms, including support vector machines (SVM), decision trees (DT), random forests (RF), and k-nearest neighbors (k-NN), were applied for predictive modeling. Unsupervised algorithms such as k-means clustering, hierarchical clustering, and principal component analysis (PCA) aided in data exploration and dimensionality reduction. Molecular docking, using structure-based algorithms (AutoDock, GOLD), and molecular dynamics simulations, employing physics-based algorithm (GROMACS), assessed binding affinities and drug-target complex stability. Pharmacokinetic/toxicity profiles were predicted using pkCSM online platform, which utilizes machine learning algorithms (graph-based signatures) for classification and regression of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. This integrative use of AI highlights its potential to minimize the experimental research costs associated with the synthesis and in vitro and in vivo testing of promising drug candidates.