Soil classification is a key aspect of precision agriculture, influencing decisions on crop selection, irrigation management, and sustainable farming practices. However, traditional machine learning (ML) and deep learning (DL) models for soil classification often face challenges such as limited data availability, class imbalance, and poor generalization across different regions. To address these issues, this study introduces a Generative Adversarial Network (GAN)-based data augmentation approach to enhance soil classification performance. GANs help overcome the lack of labeled datasets by generating high-quality synthetic soil data that closely mimics real-world soil characteristics. This synthetic data is then integrated into ML and DL models like Random Forest, XGBoost, CNNs, and ResNet to improve classification accuracy. Experimental results show that using GANs for soil classification significantly improves model performance. Compared to traditional models, GAN-augmented models achieve higher accuracy, precision, recall, and F1-score, making them more reliable for real-world applications. This research contributes to AI-driven smart agriculture by creating a more dependable, scalable, and region-specific soil classification system, helping farmers make better decisions for sustainable farming.