Across the globe, heart diseases rank as the top cause of death, with their incidence steadily rising. However, early detection before a cardiac event (e.g., cardiac arrest) remains a significant challenge. Although the healthcare sector possesses extensive data on heart disease, the effective use of this data for timely detection is essential to protect from such events. This paper proposes an innovative approach using fuzzy logic (FL), convolutional neural network (CNN) models, and feature selection to more accurately assess the risk of heart attacks. Our study also emphasizes the importance of data preprocessing, including data transformation, cleaning, and normalization, to facilitate the availability of trustworthy and high-quality information for analysis. We employed lassoCV for feature selection to identify key factors contributing to heart attack risk. Furthermore, we developed a novel 1D Convolutional Neural Network (1D-CNN) especially tailored for linear data to improve neural network training and the significant potential of advanced Artificial Intelligence (AI) techniques in revolutionizing heart attack risk estimation. We used fuzzy logic (FL) to handle data uncertainties in the risk prediction phase, enhancing prediction accuracy. Our proposed model achieved remarkable performance metrics: an accuracy of 98.5%, 100% precision, and 98.5% F1-score, which outperforms when compared with its counterparts.