Accurate estimation of iron ore reserves is critical for efficient resource management, mine planning, and economic evaluation in the mining industry. Traditional estimation methods often struggle with spatial variability and geological uncertainty, leading to potential inaccuracies. This study aims to enhance reserve estimation accuracy by applying advanced geostatistical techniques that leverage spatial data relationships. Methods including variogram analysis, ordinary kriging, and conditional simulation were employed to model the spatial distribution of iron ore grades within a representative mining site. The approach was validated using drilling data and cross-validated for prediction accuracy. Results demonstrate that geostatistical methods significantly reduce estimation errors compared to conventional techniques, capturing spatial heterogeneity more effectively. The study highlights the importance of incorporating spatial correlation structures to improve resource estimates. These findings support better-informed decision-making in mine development and resource allocation, ultimately contributing to more sustainable mining operations.