Cardiovascular disease (CVD) remains a significant worldwide health challenge, thereby necessitating precise and dependable analytical models for early risk estimation. Existing machine learning methodologies frequently exhibit performance decay restricting from the presence of redundant or irrelevant clinical features. This research familiarizes an enhanced predictive framework, incorporating Chi-Square feature selection combined with an ensemble-based classification model, with the objective of improving diagnostic accuracy and computational efficiency. The Chi-Square technique is utilized to determine statistically important attributes that contribute to identify CVD risk, thus facilitating dimensionality decrease and strengthening model interpretability. The preferred features are subsequently employed to train an improved ensemble model, which integrates Random Forest, Gradient Boosting Machine (GBM), and Extra Trees Classifier, and combines them through a soft-voting methodology. Model estimation is conducted using accuracy, precision, recall (Sensitivity), F1-score and AUC-ROC metrics to simplify a thorough assessment of performance. Initial findings recommend that the proposed Chi-Square boosted ensemble architecture surpasses predictable single classifiers and ensemble models missing feature selection, exhibiting greater predictive solidity and moderated overfitting. This study ultimately suggests that the combination of statistical feature selection with ensemble learning grants an added dependable and scalable approach for CVD risk prediction, by this means contributing significant improvement to computational healthcare and protective cardiology..