With global seismic activity on the rise, the requirement for robust earthquake prediction and early warning systems has grown in importance. This research provides a thorough examination into the creation of an Early Earthquake Warning System (EEWS) utilizing deep learning techniques, with a special emphasis on a “CNN” model by decreasing false positives. Using a diversified dataset from seismic monitoring stations, we assess the execution of the CNN model against classic machine learning models. These models include “Naive Bayes model”, “Support Vector Machine model” (SVM), “Logistic Regression model”, “Decision Tree Classifier model”, “K Nearest Neighbor Classifier”, “Random Forest Classifier”, and sophisticated neural network designs such as “Long Short-Term Memory. We examined the relative efficiency of various techniques in reliably forecasting earthquakes using rigorous testing and assessment. The findings emphasize the crucial need of developing early warning schemes, promise of “deep learning” technologies, particularly CNN, in dramatically improving prediction accuracy and limiting the negative impacts of seismic occurrences. This study contributes to ongoing efforts to create more efficient earthquake preparedness and disaster response measures, eventually protecting lives and infrastructure from the devastation caused by earthquakes.