Artificial intelligence (AI) and mobile data are transforming real-time infectious disease surveillance, offering new ways to monitor, detect, and respond to outbreaks. This paper explores the innovative applications of AI and mobile data in enhancing traditional surveillance systems. AI algorithms analyze large datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict outbreaks. Mobile health data, such as information from health apps and wearable devices, provides real-time monitoring of health indicators and geolocation data for tracking population movements. AI-driven predictive models enable timely warnings to public health authorities, allowing targeted interventions to contain disease spread. Mobile health technologies, including smartphone apps and wearable devices, collect real-time health data, such as symptoms, vital signs, and geolocation information, which can be used to track disease spread and monitor population health trends. AI also improves the efficiency of surveillance systems by automating data collection and analysis processes. However, challenges remain, including the need for high-quality data, privacy concerns, and ethical considerations. Ensuring data accuracy, interoperability, and algorithm fairness is essential for reliable AI-driven surveillance. Mobile health data raises privacy and security concerns, requiring robust data protection measures. Additionally, disparities in access to mobile devices and apps may lead to underrepresentation of certain populations in surveillance efforts. Case studies highlight the effectiveness of AI and mobile data in disease surveillance. For example, HealthMap monitors and visualizes infectious disease outbreaks, while Flu Near You tracks influenza based on self-reported symptoms. BlueDot predicted the COVID-19 outbreak in Wuhan. Mobile data has also been used to track malaria outbreaks in Senegal and Ebola in West Africa. The future of real-time infectious disease surveillance using AI and mobile data holds great promise, with advancements in technology and global collaboration driving innovation. Integration with other technologies, such as IoT and blockchain, can enhance surveillance capabilities. Global surveillance networks will facilitate data sharing and coordinated responses to health threats. However, ethical and legal frameworks must be established to ensure responsible use of AI and mobile data, protecting individual privacy and addressing data ownership issues. In conclusion, AI and mobile data have the potential to revolutionize infectious disease surveillance, offering new ways to monitor, predict, and respond to outbreaks. By addressing challenges and ethical considerations, these technologies can improve public health outcomes and protect communities worldwide.Artificial intelligence (AI) and mobile data are transforming real-time infectious disease surveillance, offering new ways to monitor, detect, and respond to outbreaks. This paper explores the innovative applications of AI and mobile data in enhancing traditional surveillance systems. AI algorithms analyze large datasets, including demographic information, travel patterns, and social media activity, to detect early signs of disease emergence and predict outbreaks. Mobile health data, such as information from health apps and wearable devices, provides real-time monitoring of health indicators and geolocation data for tracking population movements. AI-driven predictive models enable timely warnings to public health authorities, allowing targeted interventions to contain disease spread. Mobile health technologies, including smartphone apps and wearable devices, collect real-time health data, such as symptoms, vital signs, and geolocation information, which can be used to track disease spread and monitor population health trends. AI also improves the efficiency of surveillance systems by automating data collection and analysis processes. However, challenges remain, including the need for high-quality data, privacy concerns, and ethical considerations. Ensuring data accuracy, interoperability, and algorithm fairness is essential for reliable AI-driven surveillance. Mobile health data raises privacy and security concerns, requiring robust data protection measures. Additionally, disparities in access to mobile devices and apps may lead to underrepresentation of certain populations in surveillance efforts. Case studies highlight the effectiveness of AI and mobile data in disease surveillance. For example, HealthMap monitors and visualizes infectious disease outbreaks, while Flu Near You tracks influenza based on self-reported symptoms. BlueDot predicted the COVID-19 outbreak in Wuhan. Mobile data has also been used to track malaria outbreaks in Senegal and Ebola in West Africa. The future of real-time infectious disease surveillance using AI and mobile data holds great promise, with advancements in technology and global collaboration driving innovation. Integration with other technologies, such as IoT and blockchain, can enhance surveillance capabilities. Global surveillance networks will facilitate data sharing and coordinated responses to health threats. However, ethical and legal frameworks must be established to ensure responsible use of AI and mobile data, protecting individual privacy and addressing data ownership issues. In conclusion, AI and mobile data have the potential to revolutionize infectious disease surveillance, offering new ways to monitor, predict, and respond to outbreaks. By addressing challenges and ethical considerations, these technologies can improve public health outcomes and protect communities worldwide.