The integration of artificial intelligence (AI) and mobile health data has revolutionized real-time infectious disease surveillance, offering unprecedented insights into disease dynamics and enabling proactive public health interventions. This paper explores the innovative applications of AI and mobile data in transforming traditional surveillance systems for infectious diseases. By leveraging AI algorithms and vast amounts of data from mobile devices, researchers and public health authorities can monitor disease outbreaks in real-time with greater accuracy and efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict potential outbreaks. Mobile health data, such as information from health apps and wearable devices, provides continuous monitoring of health indicators and geolocation data for tracking population movements, enhancing early detection and rapid response to potential threats. However, this approach also presents challenges and ethical considerations. Privacy concerns regarding the collection and use of mobile health data must be addressed to protect individuals' rights. Additionally, issues related to data quality, interoperability, and algorithm bias need to be mitigated to ensure the reliability and effectiveness of AI-driven surveillance systems. Despite these challenges, the integration of AI and mobile health data holds immense promise for revolutionizing real-time infectious disease surveillance, allowing public health authorities to gain valuable insights into disease dynamics, enhance early detection capabilities, and implement targeted interventions to prevent the spread of infectious diseases.The integration of artificial intelligence (AI) and mobile health data has revolutionized real-time infectious disease surveillance, offering unprecedented insights into disease dynamics and enabling proactive public health interventions. This paper explores the innovative applications of AI and mobile data in transforming traditional surveillance systems for infectious diseases. By leveraging AI algorithms and vast amounts of data from mobile devices, researchers and public health authorities can monitor disease outbreaks in real-time with greater accuracy and efficiency. AI-driven predictive models analyze diverse datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict potential outbreaks. Mobile health data, such as information from health apps and wearable devices, provides continuous monitoring of health indicators and geolocation data for tracking population movements, enhancing early detection and rapid response to potential threats. However, this approach also presents challenges and ethical considerations. Privacy concerns regarding the collection and use of mobile health data must be addressed to protect individuals' rights. Additionally, issues related to data quality, interoperability, and algorithm bias need to be mitigated to ensure the reliability and effectiveness of AI-driven surveillance systems. Despite these challenges, the integration of AI and mobile health data holds immense promise for revolutionizing real-time infectious disease surveillance, allowing public health authorities to gain valuable insights into disease dynamics, enhance early detection capabilities, and implement targeted interventions to prevent the spread of infectious diseases.