Artificial intelligence (AI) is transforming disease understanding and drug development in healthcare by bridging the gap between disease identification and drug discovery. AI's ability to analyze vast datasets enables accurate predictions, accelerating drug development and reducing costs. This review discusses recent advances in AI applications in drug discovery, covering disease identification, target identification, screening, lead discovery, and clinical trials. It also addresses challenges and future prospects of AI in this field. AI has significantly improved clinical diagnosis, genome analysis, and personalized medicine. In disease diagnosis, AI models like 'cornet' and CNN-based systems have achieved high accuracy in detecting infectious diseases such as COVID-19, sepsis, and bloodstream infections. AI has also enhanced early detection of conditions like sepsis, diabetes, and Alzheimer's disease, with models like Sepsis Watch and IDx-DR showing promising results. In genome analysis, AI tools like DeepVariant and Fabric GEM have improved variant calling and identification of genetic disorders. These tools help in analyzing large-scale genomic data, identifying mutations, and understanding genetic variations linked to diseases. In personalized medicine, AI has enabled the development of tailored treatments based on individual genetic profiles. Platforms like CURATE.AI and IBM Watson for Oncology use AI to predict treatment outcomes and optimize drug dosages. However, challenges remain, including algorithmic bias, data privacy, and the need for large, diverse datasets. AI also has the potential to revolutionize drug discovery by accelerating target identification, screening, and lead discovery. For example, AI-based tools like NoduleX and DeepSV have shown high accuracy in predicting lung cancer and structural variations. Despite these advancements, AI in drug discovery faces challenges such as computational resource requirements, data interpretation, and integration with existing medical systems. Future developments aim to enhance AI's efficiency, accuracy, and accessibility, ensuring equitable access to personalized treatments. Overall, AI is poised to significantly impact healthcare by improving diagnostic accuracy, accelerating drug development, and enabling personalized, effective treatments.Artificial intelligence (AI) is transforming disease understanding and drug development in healthcare by bridging the gap between disease identification and drug discovery. AI's ability to analyze vast datasets enables accurate predictions, accelerating drug development and reducing costs. This review discusses recent advances in AI applications in drug discovery, covering disease identification, target identification, screening, lead discovery, and clinical trials. It also addresses challenges and future prospects of AI in this field. AI has significantly improved clinical diagnosis, genome analysis, and personalized medicine. In disease diagnosis, AI models like 'cornet' and CNN-based systems have achieved high accuracy in detecting infectious diseases such as COVID-19, sepsis, and bloodstream infections. AI has also enhanced early detection of conditions like sepsis, diabetes, and Alzheimer's disease, with models like Sepsis Watch and IDx-DR showing promising results. In genome analysis, AI tools like DeepVariant and Fabric GEM have improved variant calling and identification of genetic disorders. These tools help in analyzing large-scale genomic data, identifying mutations, and understanding genetic variations linked to diseases. In personalized medicine, AI has enabled the development of tailored treatments based on individual genetic profiles. Platforms like CURATE.AI and IBM Watson for Oncology use AI to predict treatment outcomes and optimize drug dosages. However, challenges remain, including algorithmic bias, data privacy, and the need for large, diverse datasets. AI also has the potential to revolutionize drug discovery by accelerating target identification, screening, and lead discovery. For example, AI-based tools like NoduleX and DeepSV have shown high accuracy in predicting lung cancer and structural variations. Despite these advancements, AI in drug discovery faces challenges such as computational resource requirements, data interpretation, and integration with existing medical systems. Future developments aim to enhance AI's efficiency, accuracy, and accessibility, ensuring equitable access to personalized treatments. Overall, AI is poised to significantly impact healthcare by improving diagnostic accuracy, accelerating drug development, and enabling personalized, effective treatments.