Soil erosion presents a critical challenge to agricultural sustainability in India, adversely affecting soil fertility, crop productivity, and overall ecosystem stability. This study leverages Machine Learning techniques, specifically Multivariate Regression Analysis, to systematically evaluate and identify the key factors influencing soil erosion. The dataset, sourced from IIT BHU, comprises 15 environmental and land-use variables, including rainfall intensity, soil type, land slope, vegetation cover, land-use practices, and soil texture. By analyzing these interrelated factors, the study aims to determine the most influential contributors to soil erosion. The findings highlight that land slope and runoff are the primary drivers of erosion, significantly impacting soil degradation rates. These insights offer valuable guidance for policymakers and agricultural stakeholders in formulating targeted soil conservation strategies to mitigate erosion risks. This research underscores the crucial role of data-driven methodologies in environmental analysis and sustainable land management. By integrating statistical modeling with real-world data, the study contributes to the advancement of precision agriculture and erosion control measures, ensuring long-term soil health and agricultural productivity.