Infectious/contagious diseases remain a significant global health challenge, necessitating accurate identification to mitigate their spread. The widespread adoption of wearable healthcare devices capable of continuously monitoring physiological parameters presents a unique opportunity for enhancing disease detection strategies. In this article, we develop a new data fusion-enabled explainable artificial intelligence-assisted light gradient boosting model (LightGBM) (FuXAI) model in edge networks to predict contagious disease in the early stage. The overall contributions of the proposed FuXAI model are threefold. First, we create a comprehensive health profile for each individual by employing data fusion techniques and integrating health parameters received from multiple sources. Second, we integrate a new lightweight machine learning (ML) model, the LightGBM, to predict the disease at resource-constraint edge devices over the fused data. Finally, we leverage the power of explainable artificial intelligence (XAI) to develop interpretable algorithms, integrating with the LightGBM that can identify subtle patterns and correlations within the fused data, potentially revealing early warning signs of infectious diseases and creating trust in medical professionals during decision-making. Extensive simulation results of the proposed model over the standard ML models using benchmark datasets demonstrate the effectiveness of the model.