Malaria is a life-threatening disease that affects millions of people worldwide, particularly in developing countries. Early and accurate detection of malaria is crucial for effective treatment and control of the disease. In recent years, deep learning techniques have shown promising results in various medical imaging tasks, including malaria detection and diagnosis. This paper presents a comprehensive review of deep learning applications for malaria detection and diagnosis. It covers the different stages of the malaria diagnosis pipeline, including image acquisition, pre-processing, parasite detection, and classification. The review discusses various deep learning architectures employed in malaria detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. It also highlights the challenges and limitations of existing approaches and identifies potential areas for future research. The findings of this review demonstrate the potential of deep learning techniques in improving the accuracy and efficiency of malaria detection and diagnosis, ultimately contributing to the efforts in eradicating this global health burden.