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This study investigates the application of machine learning techniques for predicting volcanic vent locations based on aeromagnetic geophysical data. Magnetic data, known to reflect subsurface geological structures, presents a valuable source of information for understanding volcanic activity. Leveraging this data, we aim to develop and validate predictive models capable of discerning the presence of volcanic vents. Through a comprehensive data analysis, feature engineering, and model training, we explore the intricate relationships between magnetic variations and volcanic vent locations. Various machine learning algorithms were evaluated for their efficacy in binary classification, with a focus on identifying areas with a high likelihood of volcanic vent presence. The Random Forest model (RFM) was adopted given its high performance metrics, achieving a prediction accuracy of 92%. Our results demonstrate the successful prediction of volcanic vent locations, with a significant correlation of 86% between the actual and predicted vent locations and a high Degree of Certainty (DC) at 97%. This research contributes to the advancement of geospatial data analysis within the field of geoscience, showcasing the potential of machine learning in interpreting and utilizing magnetic data for volcanic hazard assessment and early warning systems. The findings represent a significant step towards enhancing our understanding of volcanic dynamics and improving the predictive tools available for volcanic hazard assessment. [ABSTRACT FROM AUTHOR]