Malaria remains a significant global health challenge that affects more than 200 million people each year and disproportionately burdens regions with limited resources. Precise and timely diagnosis is critical for effective treatment and control. Traditional diagnostic approaches, including microscopy and rapid diagnostic tests (RDTs), encounter significant limitations such as reliance on skilled personnel, high costs and slow processing times. Advances in deep learning (DL) have demonstrated remarkable potential. They achieve diagnostic accuracies of up to 97% in automated malaria detection by employing convolutional neural networks (CNNs) and similar architectures to analyze blood smear images. This survey comprehensively reviews deep learning approaches for malaria detection and focuses on datasets, architectures and performance metrics. Publicly available datasets, such as the NIH and Delgado Dataset B are evaluated for size, diversity and limitations. Deep learning models which include ResNet, VGG, YOLO and lightweight architectures like MobileNet are analyzed for their strengths, scalability and suitability across various diagnostic scenarios. Key performance metrics such as sensitivity and computational efficiency are compared with models achieving sensitivity rates as high as 96%. Emerging smartphone-based diagnostic systems and multimodal data integration trends demonstrate significant potential to enhance accessibility in resource-limited settings. This survey examines key challenges and includes bias in the data set, generalization of the model and interpretability to identify research gaps and propose future directions to develop robust, scalable and clinically applicable deep learning solutions for malaria detection.