Wildfires are exorbitantly cataclysmic disasters that lead to the destruction of forest cover, wildlife, land resources, human assets, reduced soil fertility and global warming. Every year wildfires wreck havoc across the globe. Therefore, there is a need of an efficient and reliable system for real-time wildfire monitoring to dilute their disastrous effects. Internet of Things (IoT) has demonstrated remarkable evolution and has been successfully adopted in environmental monitoring domain. Therefore, timely detection and prediction of wildfires is the need of the hour. The proliferation of the IoT has been witnessed in the environment monitoring domain for detection and prediction of several environmental hazards. This research proposes an integrated IoT-fog-cloud framework for real-time detection and prediction of forest fires. Initially, a Bayesian belief network is used to detect the outbreak of wildfire at fog layer followed by real-time alert generation to the forest department offices and fire-fighting stations. Cloud layer-assisted fuzzy-based long-term wildfire prediction and monitoring is responsible for determining the susceptibility of a forest terrain to wildfire outbreak based on wildfire susceptibility index (WSI). Furthermore, WSI is used for risk zone mapping of forest terrains.