Cardiovascular disease (CVD) remains a principal origin of illness and mortality worldwide. Early detection is essential for preventing adverse outcomes; however, traditional diagnostic methods often involve complex procedures and time-consuming tests. Recent advancements in machine learning (ML) offer promising solutions to enhance the accuracy, efficiency, and accessibility of CVD diagnosis. This study investigates the application of various ML algorithms for detecting cardiovascular disease using a dataset comprising features such as age, gender, cholesterol levels, blood pressure, and electrocardiogram (ECG) results. The objective is to develop a predictive model that assists healthcare providers in assessing CVD risk. Several ML models are evaluated and the results indicate that random forest achieves better accuracy of 87%. By identifying key factors contributing to cardiovascular risk, this research highlights the potential of ML to improve early CVD detection and facilitate timely interventions. This approach could reduce the burden on healthcare systems and enable personalized treatment strategies for at-risk individuals.