The present research introduces a novel method for developing early cardiac disease diagnostics that blends Internet of Things (IoT) technologies with potent deep learning algorithms. The research demonstrates how a Convolutional Neural Network (CNN) model may effectively use sensor data to precisely detect and forecast a variety of cardiac issues. The basis of this study is a well curated dataset derived from a cohort of 30 patients monitored over a 10-day period. When it comes to identifying cardiac metrics like the Normal Beat, Premature Beat, Early Ventricular Contraction, Unexplained Beat, and Ventricular Fusion, the CNN model exhibits astounding accuracy, precision, recall, and F1 scores. The results show the possibility for early treatments to avoid significant health problems, changing healthcare from reactive to proactive and customised methods. The blending of cutting-edge technology and medical knowledge creates new opportunities for early diagnosis and intervention, revolutionising patient care. This study lays the path for a future where rapid and accurate heart health evaluations become essential to overall wellbeing by using the capabilities of the CNN model and IoT devices. Wide-ranging effects of this effort include increased life quality and the coming of a new generation with greater healthcare.