The cardiovascular disease (CVD) is a major global cause of death, early detection and prevention is essential. It has been demonstrated that machine learning techniques can significantly improve the precision and effectiveness of CVD detection. This work offers an effective machine learning algorithm-based approach for the early diagnosis of cardiovascular disease. In this paper, early detection system (EDS) uses a heterogeneous dataset with preprocessed clinical, demographic, and diagnostic features that dealt with outliers and missing values. To improve the performance of the proposed ensemble model, engineering and feature selection techniques were used. In this paper, we examined the machine learning algorithm such as Gradient Boosting, Random Forest, and NaïveBayes with the using model evaluation parameter as the accuracy of model, precision of model, recall of model, and F1 score. In order to combine the advantages of these algorithms and achieve better predicted performance, a novel ensemble model was presented. In addition, cross-validation and hyper parameter tuning were used to optimize model parameters and guarantee resilience. The suggested ensemble approach proved remarkably effective, detecting CVD with an accuracy rate higher than 96%. The proposed method effectively applies machine learning to identify cardiovascular disease risk factors early, allowing for prompt intervention and customized treatment recommendations. This study adds to the ongoing efforts to lower the death rate from cardiovascular disease and the cost of cardiovascular care. In order to improve public health outcomes, it emphasizes how important it is to use machine learning techniques in healthcare to deliver accurate and affordable solutions for the early identification and management of cardiovascular disease.