The development of advanced AI models using fusion data presents a significant opportunity to transform healthcare by integrating various data sources, including medical images, electronic health records (EHRs), genetic data, and clinical reports. By combining these data types, AI models can generate a more comprehensive understanding of a patient’s health to improve prediction accuracy, improve decision-making, and enhance medical outcomes. This chapter delves into developing AI models through multi-modal data fusion, emphasizing the key benefits of using a wide range of data to create a more holistic view of medical conditions. These models can address complex healthcare challenges, improve diagnostic accuracy, and provide more personalized treatments by leveraging complementary information from various data sources. However, there are several challenges in developing AI models with fusion data. Ensuring data quality across different sources is one of the major obstacles, as medical data is often inconsistent, incomplete, or noisy. Preprocessing and standardization techniques are essential to ensure that the data is usable for model training. Another challenge is model interpretability; for AI models to be adopted in clinical settings, they must be clear and interpretable for healthcare professionals. Additionally, the computational demands of training fusion models require optimized algorithms and powerful hardware to ensure scalability and real-time performance. Looking ahead, innovations in fusion techniques, federated learning, and personalized medicine are expected to address these challenges. Federated learning allows for secure training across decentralized data sources, while personalized medicine can tailor AI models to individual patients. These advancements will increase the effectiveness and applicability of AI models in healthcare, ultimately leading to more reliable, scalable, and personalized solutions for medical diagnosis, treatment planning, and patient care.