Drug metabolism is a critical factor in drug development, affecting the pharmacokinetics, pharmacodynamics, and toxicity of compounds. Computational approaches for predicting drug metabolites and metabolic sites have become essential for optimizing lead compounds and assessing drug safety. Traditional experimental methods, such as LC/MS, are resource-intensive, driving the adoption of computational tools that provide more efficient and cost-effective alternatives. Prediction methods can be categorized into rule-based, machine learning (ML), and deep learning (DL) models, each with its own strengths and limitations. Rule-based methods, such as META and Meteor, rely on encoded biotransformation rules, while ML and DL models utilize large datasets to predict metabolic reactions, including those mediated by cytochrome P450 enzymes. Despite their advancements, challenges remain, including limited experimental data for specific enzymes, the complexity of metabolic pathways, and the interpretability of ML/DL models. Integrating ML/DL approaches with traditional methods offers potential for improving prediction accuracy and expanding enzyme coverage. These advancements offer promising pathways for improving drug metabolism predictions, thereby facilitating the development of safer and more effective therapeutics.