Malaria is one of the most significant endemic diseases in Sub-Saharan Africa. In Low developed countries (LDCs), the scourge is further bolstered by the lack of enough skilled lab technologists in health centers to accurately detect the disease using the widely accepted gold standard Microscopy method. Thus, the need for reliable detection interventions. This explains the birth of the Topic Group (TG), Automated malaria detection using Artificial Intelligence (AI). The aim is to harness AI to automate the detection of Malaria in a more fast, accurate, and cost-effective manner. Recently emerging technologies of AI and machine learning that can learn complex image patterns have been successful in different medical image analysis tasks and can improve public health. Therefore, the TG-Malaria under the ITU/WHO Focus Group AI for Health (FGAI4H) aims to develop a standardised benchmarking approach for AI based detection of Malaria. This involves all activities related to the curation of a quality dataset, development of AI models and approaches related to malaria detection, suggestions on scoring metrics, development of a benchmarking framework, and extension of the solution to improve disease surveillance and prediction.