This paper explores the potential of deep learning in analyzing social media data to identify and support high-risk populations for HIV. The authors begin by providing an overview of the HIV epidemic and the importance of early detection and prevention. They introduce deep learning and its applications in public health, highlighting its ability to analyze unstructured data such as text, images, and videos. A literature review examines previous studies on using social media data for health surveillance and the applications of deep learning in this context, discussing challenges and limitations related to privacy, data bias, and algorithm transparency. The methodology section outlines the data collection process, including sources from Twitter and Facebook, and preprocessing steps to clean and prepare the data. The study employs various deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) Networks, to analyze social media content. Evaluation metrics such as precision, recall, and F1-score are used to assess model performance. Case studies illustrate the application of deep learning in identifying HIV-related discussions, analyzing images for risky behaviors, and tracking the spread of misinformation. Ethical considerations, including privacy, data bias, and algorithm transparency, are discussed, along with recommendations for future research and application. The paper concludes by emphasizing the potential of deep learning in HIV prevention through social media data analysis, calling for further exploration and implementation to reduce the global burden of HIV/AIDS.This paper explores the potential of deep learning in analyzing social media data to identify and support high-risk populations for HIV. The authors begin by providing an overview of the HIV epidemic and the importance of early detection and prevention. They introduce deep learning and its applications in public health, highlighting its ability to analyze unstructured data such as text, images, and videos. A literature review examines previous studies on using social media data for health surveillance and the applications of deep learning in this context, discussing challenges and limitations related to privacy, data bias, and algorithm transparency. The methodology section outlines the data collection process, including sources from Twitter and Facebook, and preprocessing steps to clean and prepare the data. The study employs various deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) Networks, to analyze social media content. Evaluation metrics such as precision, recall, and F1-score are used to assess model performance. Case studies illustrate the application of deep learning in identifying HIV-related discussions, analyzing images for risky behaviors, and tracking the spread of misinformation. Ethical considerations, including privacy, data bias, and algorithm transparency, are discussed, along with recommendations for future research and application. The paper concludes by emphasizing the potential of deep learning in HIV prevention through social media data analysis, calling for further exploration and implementation to reduce the global burden of HIV/AIDS.