Malaria remains a significant global health challenge, particularly in low-resource regions where rapid and accurate diagnosis is often lacking. This study explores the potential of artificial intelligence (AI) to enhance the detection of malaria through the analysis of hematological parameters. By leveraging machine learning (ML) and deep learning (DL) techniques, such as convolutional neural networks (CNNs) and random forests (RFs), we aim to improve diagnostic accuracy and efficiency. Our findings indicate that AI models can achieve diagnostic accuracies exceeding 95%, outperforming traditional methods like microscopy and rapid diagnostic tests (RDTs). Additionally, the integration of AI into mobile health (mHealth) applications enables real-time diagnostic support in remote areas, significantly increasing diagnostic accessibility. The study also highlights the potential of combining AI with clinical decision support systems (CDSS) and molecular diagnostic techniques, such as polymerase chain reaction (PCR), to create a more comprehensive and reliable diagnostic framework. While these advancements offer promising avenues for improving malaria detection and management, challenges such as model interpretability and infrastructure development for low-resource settings remain. This paper emphasizes the need for collaborative efforts to refine AI models and integrate them into existing healthcare systems to combat malaria effectively.