The study investigates the potential of wearable technology for early malaria detection in Siaya, Kenya. Utilizing wearables to monitor vital signs, the research employs machine learning models such as GAN with LSTM, NCP, and WGAN for data analysis. Initial results show promise for early detection in low-resource settings. Challenges include data variability and the need for larger datasets. Future work will focus on model refinement and broader population studies to develop a scalable tool for resource-limited health systems.