The readmission of patients into hospitals has been considered a measure of the quality of care delivered, the quality of discharge planning, and continuity of care. In Saudi Arabia, different digital health initiatives and advancements in electronic health record systems offer a timely opportunity to synthesize evidence on different artificial intelligence-based models for predicting hospital readmission risk and identify key design features of the model that are clinically relevant and useful. In this article, we have synthesized evidence on different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes using different machine learning and deep learning techniques and different explainable artificial intelligence techniques for predicting short-term hospital readmission risk and healthcare utilization outcomes within a 7-30-day window using peer-reviewed articles published between 2020 and 2025. The evidence on different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes using different machine learning and deep learning techniques and different explainable artificial intelligence techniques is discussed in detail with special emphasis on its applicability to hospital systems in Saudi Arabia. The gradient boosting and tree-based ensemble techniques are found to be consistently performing well for structured electronic health record-based predictive models. The representation learning-based techniques are found to be performing well for structured electronic health record-based predictive models. The outcome definition heterogeneity, class imbalance, temporal leakage, and lack of external validation are some of the limitations identified in different studies on artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes. For hospital systems in Saudi Arabia, different factors need to be considered for developing artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes. The methodological blueprint for hospital systems in Saudi Arabia is proposed using different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes based on different machine learning and deep learning techniques and different explainable artificial intelligence techniques using the PRISMA 2020 protocol and TRIPOD+AI protocol for systematic reviews and reporting of prediction model performance.