Introduction/Background: Despite country-wide surveys and monitoring HIV/AIDS infections, the true prevalence can be hidden for areas with small population densities and lack of data, especially among vulnerable groups. Accurate estimation of HIV/AIDS prevalence is essential for tailoring effective interventions and resource allocation. Traditional survey-based methods for estimating disease prevalence often face limitations, particularly in small geographical areas where data may be sparse or unavailable. Materials/Methods: This study applied a novel approach, leveraging a Neural method, an advanced machine learning technique for small area estimation (SAE), using the 2017 SABSSM and 2011 South Africa Census dataset. SAE was trained using both the "Neural method" and "Genetic method," and the data was split randomly on a number of different iterations (70% training - 30% training; 50% testing - 50% testing; 80% training - 20% testing) until the best and desirable output was achieved. Conclusion: By providing granular and up-to-date estimates of HIV/AIDS prevalence, this approach assists in the ongoing efforts to combat HIV/AIDS in South Africa. Keywords: Acquired immunodeficiency syndrome, Health status disparities, Epidemiology, South Africa, Neural networks, Disease, Prevalence. Results: The findings from the study highlight the feasibility of the model in obtaining reliable HIV/AIDS prevalence estimates in South Africa at the enumeration area across nine provinces, with an average correlation of 0.88 and R2 = 0.82. Overall, from all provinces, race was found to be significant in predicting HIV/ADS prevalence, followed by urban geographic location and sex. Discussion: These results can help in identifying high HIV/AIDS prevalent areas to inform location-based or geotargeted interventions and policies to efficiently reduce the spread of HIV/AIDS in South Africa. The research contributes to the advancement of SAE techniques in the context of public health, showcasing the potential of artificial intelligence and machine learning to address pressing health challenges. By providing granular and up-to-date estimates of HIV/AIDS prevalence, this approach assists in the ongoing efforts to combat HIV/AIDS in South Africa.