Global Warming is one of the most threatening climatic challenges faced in the current era. It has led to numerous natural disasters among which GLOF is one of the most dangerous consequences. Climatic enthusiasts have graded GLOFs as one of the biggest reasons for loss of human life. The unreliability of current remote sensing systems, the unavailability of large-scale datasets, connectivity issues and divergence in tolerance of alerting devices has led to unpredictable losses in human lives. This research proposes a hybrid integration of IoT and ML models by building an IoT-based early warning system for GLOFs. The dataset used for predictive analysis of GLOFs is the Glacier Lake Outburst Flood Database V3.0. Further, ML models which include Logistic Regression, Random Forest Classifier, and XGBoost have been implemented on the IoT system data to promote a comprehensive prediction of GLOF events. Based on the comparative analysis, XGBoost yields better results with an accuracy score of 0.95, due to its unique abilities to recognize patterns and dissimilar non-regularities that signal an elevated flood risk. A PSO optimized convolutional neural network (CNN) model, when used with satellite imagery data, has performed early predictive analysis of GLOFs with an accuracy score 0.90. The final GLOF prediction is obtained by integrating the predictions of the two individual subsystems using a weighted majority voting classifier. As time progresses, there are chances of sensor failure, but this limitation is handled by avoiding single point of failures by using multiple subsystems. Therefore, the proposed system assists in providing early, reliable GLOF prediction, paving way for enhanced and reliable disaster management.