This study investigates the interconnectedness between sleep disorders and heart disease by employing a machine learning model trained on a comprehensive dataset encompassing sleep health and lifestyle factors. Validation of the model’s predictive capabilities is conducted using a heart disease-specific dataset, with a specific emphasis on understanding the association between sleep disorders and cardiovascular health. Key factors influencing sleep disorders are identified and normalized. Four machine learning algorithms are deployed, with model performance assessed using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. Data integration involves the utilization of a trained Support Vector Machine (SVM) model to predict sleep disorders in heart disease dataset (Cardio), which is then integrated with sleep health and lifestyle dataset to create new dataset. Statistical methods, including joint probability distribution, chi-square testing, and Fisher’s exact testing, are employed to study the relationship between heart disease and sleep disorders, particularly among male patients. Visualization techniques utilizing new dataset, such as contour plot, count plot, offer graphical insights into the relationship between heart disease and sleep disorders. The study validates sleep and heart disease association through rigorous hypothesis testing and robust statistical analyses. Our findings underscore the critical role of sleep quality in cardiovascular health, offering actionable insights for healthcare policies and interventions.