Agriculture is the cornerstone of regional economies, with vast expanses of land increasingly transformed by machinery striving to meet the demands of a growing population. Ensuring optimal crop nourishment requires healthy soil because poor soil conditions lead to suboptimal yields. Precise spatial prediction and digital documentation of soil properties are essential for precision agriculture, enabling accurate nutrient management and fostering the development of “intelligent soils.” Advancements in machine learning include revolutionizing computational methods for land characterization. This study aimed to enhance soil prediction through algorithmic approaches, examining soil composition and traits, property forecasting, the execution of previously accessible datasets and maps, the influence of soil quality on crop growth, and communication systems that support innovative farming. To evaluate the effectiveness of the proposed methods, models combining deep learning and machine learning approaches were employed. The results demonstrated that ResNet50 + SVM achieved the highest accuracy 92.4%, and AlexNet + SVM and SqueezeNet + SVM yielded accuracies of 88.1% and 85.3%, respectively, highlighting the potential of deep learning for soil property prediction. This analysis provides the potential of smart agriculture to improve both crop quality and productivity.