Drug Discovery and Development are complex and expensive processes with time-consuming aspects. There are many reasons for the failure of drug discovery and development, including lack of efficacy, toxicity, and poor pharmacological properties of potential drugs. Computer-Aided Drug Design (CADD) and In Silico methods have become more popular in recent years, offering scientists a better alternative than traditional experimental methods. These new technologies incorporate computer-based modeling with bioinformatics and cheminformatics to improve the effectiveness of the drug discovery and development process in a shorter amount of time. As such, this review focuses on the major in silico drug discovery strategies, such as Target Discovery, Ligand-Based vs Structure-Based Drug Design, Molecular Docking, Virtual High-Throughput Screens (vHTHps), Quantitative Structure-Activity Relationship Analysis (QSAR), and ADMET Prediction. It will also explain how bioinformatics and Molecular Modeling help identify and optimize potential leads for drug discovery, along with describing the latest advancements in Artificial Intelligence and Machine Learning that are helping improve the overall process. Finally, this review will discuss the common challenges, limitations, and future directions related to computational drug design. Ultimately, utilizing in silico approaches will save time, save money, and reduce the overall costs associated with drug discovery and development, making them indispensable parts of the modern drug discovery and development landscape