AI techniques, including machine learning algorithms, neural networks, and deep learning architectures, have significantly enhanced the accuracy and efficiency of molecular structure prediction, quantum chemistry calculations, and molecular dynamics simulations. In drug discovery, AI facilitates virtual screening, de novo drug design, and pharmacophore modeling, accelerating the identification and optimization of novel therapeutics. Furthermore, AI driven approaches enable predictive synthesis planning, reaction outcome prediction, and property estimation with unprecedented precision, thereby streamlining synthetic route design and optimization of molecular properties for diverse applications. Methodological advancements encompass a spectrum of techniques, from traditional machine learning models like support vector machines and random forests to state-of-the-art deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs),