Soil contamination poses a significant environmental and public health risk, particularly when hazardous metal concentrations exceed safe thresholds. This study aims to predict the future spread of soil contamination using 3D modeling based on empirical data collected from multiple sampling locations. The dataset includes key environmental parameters such as dissolved oxygen (DO), temperature, and atmospheric pressure, alongside spatially referenced measurements of hazardous metal concentrations (e.g., arsenic, lead, cadmium, chromium, nickel, and copper). These data points were geolocated using high-accuracy GPS mapping, ensuring precise spatial representation. Analysis of the collected samples indicates that lead (Pb) and arsenic (As) exhibit particularly high concentrations, with Pb ranging up to 142.9 mg/kg and As reaching 45.4 mg/kg in certain hotspots. Industrial and agricultural land use significantly influences contamination levels, with industrial sites showing very high contamination factors (CF ≥ 6) for Pb and high CF values for As. Agricultural zones exhibit moderate to high CF values for both As and Pb, primarily due to pesticide and fertilizer application. Environmental factors such as dissolved oxygen levels (as low as 1.16 mg/L) and fluctuating temperatures (ranging from 15.9°C to 21.2°C) further influence the migration of contaminants. The study reveals that soil quality varies across locations, with elevated concentrations of heavy metals such as cadmium (Cd), chromium (Cr), and zinc (Zn), presenting additional ecological concerns. By leveraging 3D modeling techniques, we analyze the spatial distribution and potential diffusion of contaminants over time, taking into account environmental factors influencing soil migration patterns. The model predicts the expansion of contamination zones, particularly in industrial and agricultural areas, emphasizing the need for targeted remediation efforts. The results highlight the urgent need for proactive soil remediation measures in high-risk areas. By integrating field data with computational modeling, this study offers a robust approach for forecasting contamination spread, optimizing land-use planning, and minimizing ecological risks.