Cardiovascular disease (CVD) is the foremost cause of mortality globally, responsible for around 17.9 million deaths in 2016, with projections by the World Health Organization indicating an increase to over 23.6 million by 2030. Early detection is vital to curbing CVD-related deaths. While commonly used diagnostic tools like electrocardiograms (ECGs) are limited in detecting structural heart issues, advanced imaging technologies such as echocardiograms, cardiac MRIs, and CT scans are effective but expensive and require specialized personnel, making them less accessible in many low- and middle-income countries, where 80% of CVD fatalities occur. This chapter investigates the creation of an AI-powered diagnostic system using phonocardiograms (PCGs) to identify heart diseases. By utilizing the widespread availability of smartphones and wearable devices, the system aims to offer early and precise diagnoses without needing advanced medical infrastructure. The research includes gathering a diverse collection of PCG recordings, preprocessing the data, and training an artificial intelligence (AI) model with deep learning algorithms to analyze heart sounds and identify potential heart conditions. The trained model will be deployed in a user-friendly mobile application. The proposed solution is a cost-effective and accessible tool for use in both clinical settings and at home, reducing the burden on healthcare professionals and making diagnostic services more accessible in resource-limited areas. This approach ensures timely detection and treatment of heart diseases, ultimately aiming to decrease CVD mortality rates.