Artificial Intelligence (AI) has emerged as a transformative technology with significant potential to revolutionize disease understanding and drug design in healthcare. AI serves as an accelerating tool that bridges the gap between understanding diseases and discovering drugs, leveraging its capacity to analyze and interpret large datasets with impressive accuracy. This review comprehensively examines recent advances in AI and its applications in drug discovery and development, covering stages from disease identification to clinical trials. It highlights the benefits of AI in expediting the drug discovery process and reducing costs, while also discussing the challenges and future prospects of this field. The classical drug discovery process is lengthy and expensive, often taking 10 to 15 years and costing around $161 million to $4.54 billion. Despite these efforts, nearly 90% of potential drug candidates fail in clinical trials due to issues such as reduced efficacy, poor pharmacokinetic properties, or adverse side effects. AI and machine learning (ML) have been developed and implemented at various stages of drug design to address these challenges. Key AI methods used in drug discovery include supervised learning, unsupervised learning, semi-supervised learning, ensemble learning, and deep learning. Supervised learning, which requires labeled datasets, is central to drug discovery, enabling the prediction of biological activity of novel compounds. Examples of supervised learning algorithms include Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes. Unsupervised learning deals with unlabeled data, aiming to uncover underlying structures and reduce dimensionality. Semi-supervised learning combines supervised and unsupervised learning, useful when labeled data is limited. Deep learning, focusing on artificial neural networks, is effective in handling complex problems like natural language processing and speech recognition. AI has significantly advanced the diagnosis of various diseases, including infectious diseases, lifestyle disorders, neurodegenerative disorders, and cancer. For instance, AI-powered diagnostic tools have shown high accuracy in detecting COVID-19, sepsis, diabetes, Alzheimer's disease, and cancer. In genome analysis, AI tools like DeepVariant and DeepTrio have improved the prediction of genomic variants, while platforms like CURATE.AI and IBM Watson for Oncology have enhanced personalized medicine by predicting treatment responses and optimizing dosing. However, challenges remain, including algorithmic bias, the need for high-quality data, and ethical considerations in personalized medicine. Despite these challenges, the integration of AI in drug discovery shows promise in accelerating the development of effective and personalized treatments.Artificial Intelligence (AI) has emerged as a transformative technology with significant potential to revolutionize disease understanding and drug design in healthcare. AI serves as an accelerating tool that bridges the gap between understanding diseases and discovering drugs, leveraging its capacity to analyze and interpret large datasets with impressive accuracy. This review comprehensively examines recent advances in AI and its applications in drug discovery and development, covering stages from disease identification to clinical trials. It highlights the benefits of AI in expediting the drug discovery process and reducing costs, while also discussing the challenges and future prospects of this field. The classical drug discovery process is lengthy and expensive, often taking 10 to 15 years and costing around $161 million to $4.54 billion. Despite these efforts, nearly 90% of potential drug candidates fail in clinical trials due to issues such as reduced efficacy, poor pharmacokinetic properties, or adverse side effects. AI and machine learning (ML) have been developed and implemented at various stages of drug design to address these challenges. Key AI methods used in drug discovery include supervised learning, unsupervised learning, semi-supervised learning, ensemble learning, and deep learning. Supervised learning, which requires labeled datasets, is central to drug discovery, enabling the prediction of biological activity of novel compounds. Examples of supervised learning algorithms include Support Vector Machines (SVM), Random Forest (RF), and Naive Bayes. Unsupervised learning deals with unlabeled data, aiming to uncover underlying structures and reduce dimensionality. Semi-supervised learning combines supervised and unsupervised learning, useful when labeled data is limited. Deep learning, focusing on artificial neural networks, is effective in handling complex problems like natural language processing and speech recognition. AI has significantly advanced the diagnosis of various diseases, including infectious diseases, lifestyle disorders, neurodegenerative disorders, and cancer. For instance, AI-powered diagnostic tools have shown high accuracy in detecting COVID-19, sepsis, diabetes, Alzheimer's disease, and cancer. In genome analysis, AI tools like DeepVariant and DeepTrio have improved the prediction of genomic variants, while platforms like CURATE.AI and IBM Watson for Oncology have enhanced personalized medicine by predicting treatment responses and optimizing dosing. However, challenges remain, including algorithmic bias, the need for high-quality data, and ethical considerations in personalized medicine. Despite these challenges, the integration of AI in drug discovery shows promise in accelerating the development of effective and personalized treatments.