This article presents a comprehensive study on predictive analytics in the healthcare domain, with a focus on diabetes prediction as a working example. The study emphasizes the integration of Recursive Feature Elimination (RFE) for feature engineering and Support Vector Machine (SVM) for machine learning. The primary objective is to enhance predictive accuracy by leveraging innovative feature engineering techniques and advanced machine learning algorithms. The research begins with meticulous data preparation, followed by the application of RFE to identify and select the most significant features from a dataset containing various diabetes-related parameters. These features are then utilized to train an SVM model, chosen for its effectiveness in handling both linear and nonlinear data. The evaluation process includes detailed metrics such as accuracy, precision, recall, and F1-score, providing a comprehensive assessment of predictive capabilities. Additionally, hyperparameter tuning is conducted to further optimize the model's performance. The successful deployment of this predictive analytics model demonstrates its potential as a valuable tool in early diabetes detection and management, contributing to improved healthcare outcomes. Through this research, we underscore the importance of predictive analytics and feature engineering in healthcare, offering insights into practical applications and advancements in disease prediction