The research presents the development and validation of an Enhanced XGBoost-Support Vector Machine (XGBS) Model for crop recommendation in Karnataka based on soil and environmental parameters. By obtaining a dataset encompassing variables such as soil nutrients (N, P, K), temperature, humidity, pH, and rainfall, the study embarked on pre-processing, including median imputation for missing values and outlier correction through IQR, followed by feature normalization. Exploratory data analysis revealed distinct phosphorus, potassium, and rainfall requirements across various crops, with relational plots indicating diverse nutrient tolerances. The Enhanced XGBS Model, integrating the strengths of XGBoost and SVM, demonstrated superior predictive performance, with an overall accuracy of 99.31%, outshining conventional models in precision, recall, and F1-score metrics across all crops. This ensemble model serves as a robust framework, offering significant improvements over traditional methods and showcasing potential for precise agricultural decision-making, optimized resource utilization, and enhanced crop yield outcomes.