Volcanic eruptions pose significant threats to human life, infrastructure, and the environment. Timely prediction and accurate forecasting of volcanic activity are crucial for implementing effective disaster management strategies. In this research, we leverage machine-learning techniques to analyze seismic activity data and forecast volcanic eruptions. Our research focuses on comparing the predictive performance of Support Vector Machine (SVM), Logistic Regression, and Gaussian Classifier models with our proposed Random Forest model and evaluating its performance. Through comprehensive data analysis and model evaluation, we demonstrate the superiority of Random Forest in terms of accuracy and reliability for volcanic eruption prediction. Visualizations of global earthquake patterns and dataset attribute variations provide valuable insights into seismic activity patterns and their correlation with volcanic eruptions. The research findings underscore the potential of machine learning in enhancing volcanic monitoring systems and contributing to early warning mechanisms for volcanic hazards.