The occurrence of earthquakes, which are triggered by continuous tectonic movements and the release of accumulated strain energy, is one of the most devastating natural disasters. Despite the unpredictability of these events, advancements in the field of instruments have allowed us to record precise data and information related to earthquakes in terms of magnitude, ground shaking, and location. Hence, this information can be utilized by integrating it with advanced machine learning algorithms to predict earthquakes in real-time. Machine learning has proven promising in various fields, such as health care, transportation, and information technology. Recent machine learning applications in earthquake engineering have helped predict ground motion and simulate seismic activity. However, the full potential of machine learning in earthquake forecasting is yet to be explored especially in the Indian context. Therefore, this study aims to develop a real-time prediction model for earthquakes using machine learning algorithms by analyzing data from a comprehensive earthquake catalog for highly active seismic regions like the Himalayas. The study will identify critical parameters from selected seismic characteristics and apply machine learning models like artificial neural networks (ANN), support vector regressors (SVR), and hybrid neural networks (SVR-HNN) to predict earthquakes of magnitude 5 and above for the Western Himalayas. The proposed study will greatly assist in identifying potential seismic hazard regions and developing policies to better prepare for future earthquakes.