Malaria remains a major public health challenge, with more than 260 million cases and nearly 600 000 deaths reported in 2023 alone. Although the control strategies have been extensively used, lack of detection, increasing resistance to antimalarial drugs, and insufficient surveillance remain a barrier. Artificial Intelligence (AI) has emerged as a transformative tool for malaria diagnosis, treatment, and control. This narrative review summarizes recent developments in the integration of machine learning (ML) and deep learning (DL) in malaria research and clinical workflows. Image-based diagnostic systems that utilize Convolutional Neural Networks (CNNs), YOLO, and hybrid Vision Transformer (ViT) architectures give expert-level (>95%) accuracy in parasite detection and species classification. On the other hand, non-imaging diagnostic methods based on infrared spectroscopy, MALDI-TOF MS, and AI-assisted rapid diagnostic tests provide quick and reagent-free diagnosis. At the therapeutic level, AI promotes drug discovery and repurposing via predictive modeling of drug–target interactions and personalized medicine by predicting outcomes for each patient. In the surveillance context, ML frameworks and Explainable AI (XAI) improve outbreak forecasting, vector identification, and geospatial risk mapping, contributing to effective early-warning and more precise control techniques. Finally, the paper discusses persistent challenges, including poor data, inadequate infrastructure, and ethical challenges, before exploring the intersection of AI and Internet-of-Medical-Things (IoMT) systems for intelligent malaria management. Future research needs to prioritize transparency, equity, and sustainability to provide in situ, explainable, and applicable AI tools for malaria elimination.