Artificial intelligence (AI) is transforming HIV self-testing (HIVST) by enhancing diagnostic accuracy, improving posttest support, and streamlining linkage to care. AI-powered computer vision algorithms have achieved 100% sensitivity in HIVST result interpretation, which has significantly minimized the risk of human errors. AI-enabled systems facilitate real-time result retrieval and automated follow-ups. It also ensures timely linkage to prevention and treatment services. Also, AI-driven multiplex diagnostics empower program managers to simultaneously detect HIV, syphilis, and hepatitis B/C. It offers a cost-effective and efficient approach to infectious disease testing. However, barriers like false-positive results, gaps in digital literacy , privacy concerns, and equitable access to AI-based self-testing persist. Though AI holds transformative potential for HIVST and infectious disease diagnostics, its implementation requires careful consideration of ethical, logistical, and technological challenges. Future research should focus on refining AI-driven diagnostic algorithms, expanding AI accessibility in low-literacy populations, enhancing privacy safeguards, and integrating AI-assisted self-testing into national health care infrastructures.