Artificial intelligence (AI) is transforming HIV treatment by enabling personalized and optimized care through advanced data analysis, predictive analytics, and real-time monitoring. This study explores the role of AI in tailoring HIV treatment regimens to individual patient needs, improving treatment outcomes, and addressing challenges such as data privacy, ethical considerations, and regulatory oversight. AI-driven optimization uses machine learning algorithms to analyze vast amounts of patient data, including genetic information, viral load, and treatment history, to identify effective treatment strategies. By considering factors like drug resistance, comorbidities, and lifestyle, AI can recommend personalized treatment options, leading to better adherence and clinical outcomes. Real-time monitoring allows for rapid adjustments to treatment regimens, helping to prevent treatment failure and drug resistance. However, challenges such as data quality, algorithm bias, and the need for robust validation and regulatory frameworks must be addressed to ensure the safe and effective implementation of AI in clinical practice. The integration of AI into HIV treatment has the potential to revolutionize care by improving patient outcomes, reducing the burden of disease, and advancing global efforts to combat HIV/AIDS. Future research should focus on advancing AI technologies, enhancing collaboration among stakeholders, and ensuring equitable access to AI-driven solutions in HIV treatment.Artificial intelligence (AI) is transforming HIV treatment by enabling personalized and optimized care through advanced data analysis, predictive analytics, and real-time monitoring. This study explores the role of AI in tailoring HIV treatment regimens to individual patient needs, improving treatment outcomes, and addressing challenges such as data privacy, ethical considerations, and regulatory oversight. AI-driven optimization uses machine learning algorithms to analyze vast amounts of patient data, including genetic information, viral load, and treatment history, to identify effective treatment strategies. By considering factors like drug resistance, comorbidities, and lifestyle, AI can recommend personalized treatment options, leading to better adherence and clinical outcomes. Real-time monitoring allows for rapid adjustments to treatment regimens, helping to prevent treatment failure and drug resistance. However, challenges such as data quality, algorithm bias, and the need for robust validation and regulatory frameworks must be addressed to ensure the safe and effective implementation of AI in clinical practice. The integration of AI into HIV treatment has the potential to revolutionize care by improving patient outcomes, reducing the burden of disease, and advancing global efforts to combat HIV/AIDS. Future research should focus on advancing AI technologies, enhancing collaboration among stakeholders, and ensuring equitable access to AI-driven solutions in HIV treatment.