The increased incidences of infectious diseases such as malaria, attributed to climate change, pose a growing challenge to health systems across the globe (World Health Organization, 2022). These challenges are more pronounced in some regions, particularly, in Sub-Sahara Africa (SSA), where current infectious disease surveillance systems are limited (World Health Organization, 2022). The toll of infectious diseases will tragically increase, especially in the context of climate change (Gaddy, 2020). The integration of IKSs with advanced Machine Learning (ML) and climate data presents a novel approach that may enhance the robustness of early warning systems for infectious diseases. Our research study uses ML, climate data and indigenous knowledge (IK) to develop a malaria early warning system. This approach addresses a critical gap in local health responses to infectious diseases such as malaria. By boosting IK with computational power, this study develops a more accurate and contextually relevant disease prediction system that may contribute to reducing outbreaks. Furthermore, the research study informs policy decisions to mitigate and manage malaria outbreaks.