Machine Learning Application in Enhancing HIV Management andTreatment Outcomes: Revolutionizing HIV Infection

HIV/AIDS constitutes a significant global health challenge, impacting more than 38 million individuals across the world, and continues to put pressure on healthcare systems, especially within low- and middle-income nations. Despite significant progress in Antiretroviral Therapy (ART), challenging obstacles remain, including delayed diagnoses, poor treatment adherence, and the emergence of drug resistance. This review investigates the transformative prospects presented by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to offer new aspects in HIV/AIDS prevention, diagnosis, and treatment, highlighting how these technologies can facilitate early detection, optimize personalized therapeutic strategies, and expedite drug discovery or repurposing. By combining diverse ML methodologies such as supervised, unsupervised, and reinforcement Learning Model (LM), alongside DL frameworks that include convolutional and recurrent neural networks, recent investigations have realized enhancements in the accuracy of diagnose, real-time monitoring, and personalized therapeutic approaches. Furthermore, emerging innovations such as pharmacogenomics-driven modeling, digital twin technology, and AI-powered virtual screening platforms are set to significantly expedite the identification of novel antiviral agents while optimizing ART regimen selection. These advancements improve patient-specific outcomes and contribute to extensive public health strategies by facilitating predictive epidemiological modeling, forecasting transmission dynamics, and optimizing resource allocation in areas of high-burden settings. By matching state-of-the-art computational techniques with clinical and public health methodologies, this review highlights the profound potential of AI-driven interventions to substitute more effective, equitable, and adaptable responses in the global effort against HIV/AIDS. Ultimately, the exploitation of AI and ML methodologies presents a viable pathway toward reconciling existing healthcare disparities and shaping a future characterized by precision medicine in HIV/AIDS management.