Hospital readmissions are a critical challenge in healthcare systems, leading to increased healthcare costs and patient burden. Artificial intelligence (AI) techniques have emerged as powerful tools for predicting hospital readmissions and improving patient outcomes. This chapter presents an overview of the application of AI in hospital readmission forecasting, discussing the benefits, challenges, and future directions in this field. Various AI techniques, including machine learning and deep learning algorithms, have been employed to analyze diverse patient data and identify risk factors associated with readmissions. These models leverage large-scale datasets, capturing complex patterns and relationships in patient demographics, clinical variables, and previous healthcare utilization. By integrating these features, AI algorithms can accurately predict the likelihood of readmission, enabling healthcare providers to intervene and provide timely care to high-risk patients. Hence, AI techniques offer significant promise in hospital readmission forecasting, enabling proactive interventions and improved patient care. While challenges exist, such as data availability and model interpretability, collaborative efforts and advancements in AI technology can overcome these hurdles. By harnessing the power of AI, healthcare systems can effectively reduce readmission rates, optimize resource allocation, and enhance patient outcomes. This chapter presents the use of AI techniques in hospital readmission forecasting, highlighting their applications, benefits, and challenges in improving patient outcomes and reducing healthcare costs.