aspg-28-3351-1792155165 20261016125245 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2024 8 2 Bahaa El-Din Waleed Department of Applied Health Sciences, Higher Technological Institute of Applied Health Sciences, Mansoura, Egypt El-Sayed M. El-Kenawy Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt; School of ICT, Faculty of Engineering, Design and Information & Comm Sherif Ibrahim Faculty of Medicine, Mansoura University, Egypt Asmaa H.rabie Department of Computers and Control, Faculty of Engineering, Mansoura University, Mansoura, Egypt Hossam El-Din Moustafa Department of Communications and Electronics, Faculty of Engineering, Mansoura University, Egypt Heart attacks, or myocardial infarctions, are a primary cause of mortality worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. This paper reviews various Artificial Intelligence (AI) and Machine Learning (ML) techniques for heart attack diagnosis, focusing on both traditional algorithms and more complex models. The traditional algorithms are Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Decision Trees (DT). More complex models are Convolutional Neural Networks (CNN), Extreme Gradient Boosting (XGBoost), Auto-encoders, Artificial Neural Networks (ANN), and TSK Fuzzy Inference System (TANFIS). Additionally, the integration of optimization techniques, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Jellyfish Optimization Algorithm (JOA) is explored to enhance model accuracy by selecting the most important features. Our findings indicate that ensemble and hybrid models, which combine ML with metaheuristic optimization, show significant potential in improving diagnostic performance and reducing overfitting. However, challenges remain, particularly regarding computational complexity and interpretability. This study provides insights into the strengths and limitations of different AI-based diagnostic models, contributing to the advancement of automated heart disease prediction systems. 2024 19 26 3351 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.080203 https://www.americaspg.com/journal/28/article/3351 https://www.americaspg.com/storage/81733416834.pdf