Land bonitation is a critical process for assessing land quality, particularly for agricultural and sustainable use, based on factors like soil quality, agricultural potential, and management practices. This article presents an innovative approach that integrates Geographic Information Systems (GIS) with Romanian bonitation standards to improve agricultural land assessment accuracy. The study leverages GIS with crop yield estimation models, remote sensing imagery, and crop simulation, demonstrating robust performance in yield estimation and optimizing water efficiency. The proposed algorithm uses GIS data to calculate bonitation scores based on 17 class indicators, facilitating the evaluation of land suitability for agricultural purposes. Experimental results highlight how grid point density affects computation time and demonstrate the efficacy of machine learning techniques in predicting environmental factors such as humidity. The study underscores the importance of GIS-based bonitation in tackling land fragmentation and environmental variability. Future research will focus on integrating advanced machine learning techniques for more precise assessments and improved crop production.