Digital technology represents a fundamental pillar in the development of infectious disease management strategies, significantly enhancing early surveillance mechanisms, rapid response systems, and large-scale epidemiological data analysis. With the rapid advancement of communication technologies and intelligent computing systems, it has become possible to integrate individual and collective health data into advanced digital platforms that support decision-makers and strengthen the effectiveness of public health interventions. Mobile phone applications play a pivotal role in tracking infectious diseases by collecting real-time health data and linking it to centralized databases that enable the monitoring of suspected and confirmed cases. These applications rely on geolocation technologies, Bluetooth systems, and contact data analysis to identify potential transmission chains and quickly notify individuals who have been exposed, thereby reducing community transmission rates. Their importance became particularly evident during the COVID-19 pandemic, when many countries adopted such applications to trace contacts, provide health guidance, and update daily statistics related to infections and recoveries. These applications also enhance public health awareness by sending instant notifications regarding preventive measures, vaccination schedules, and epidemiological updates. Furthermore, they allow users to self-report symptoms, assisting health authorities in detecting early patterns of disease spread. Cloud-based database systems support the integration of information between healthcare institutions and laboratories, improving the accuracy of epidemiological reports and accelerating decision-making processes. With regard to artificial intelligence, advanced techniques in machine learning and big data analytics have transformed the understanding of epidemic dynamics. AI models can process vast and diverse datasets—including travel records, population density, traffic mobility patterns, environmental conditions, and healthcare records—to build predictive models that simulate infection spread scenarios. These models contribute to estimating reproduction rates, identifying potential hotspots, and evaluating the effectiveness of preventive interventions such as lockdowns and vaccination campaigns. Artificial intelligence algorithms also enhance epidemiological data analysis by uncovering hidden patterns and complex relationships among multiple variables, surpassing traditional analytical approaches. They can improve early diagnosis accuracy through the analysis of symptoms and clinical data, and support public health centers in the efficient allocation of medical resources, such as ventilators and intensive care unit beds, based on projected increases in case numbers. Despite the significant advantages of these technologies, challenges remain concerning privacy protection and data security. The collection of sensitive health information requires strict legal and ethical frameworks to ensure that data are used exclusively for public health purposes. Moreover, the effectiveness of these systems depends on the accuracy of input data and users’ compliance with digital health applications. The integration of mobile applications with artificial intelligence systems represents a comprehensive model for managing infectious diseases in the digital era, combining real-time monitoring, predictive analysis, and strategic decision support. With continued advancements in data technologies and cloud computing, these tools are expected to become essential components of public health systems, enhancing epidemic preparedness and reducing the health and economic impacts of infectious diseases in the future. Al-Mustaqbal University — The First University in Iraq.