Deep learning is one of the most well-known machine learning methods in various sciences and industries worldwide. With the passage of time and the increase of data by humans, analyzing this data, including numbers, images, signals, and sounds, has increased the importance of Artificial Neural Networks (ANNs). However, with a detailed understanding of ANNs , one can think about the strengths and weaknesses of this widely used field in artificial intelligence. Several deep learning methods based on ANNs have been introduced and explained in this research. Then, cardiac arrhythmia has been discussed. A dataset of cardiac arrhythmia disease has been introduced to build a bridge between cardiac arrhythmia disease and the field of artificial intelligence and computers. In addition, the submitted dataset has been expertly examined, and all the features and types of cardiac rhythms present in it have been explained. Furthermore, metaheuristic optimization algorithms have been employed to improve the accuracy and performance of deep learning models in cardiac arrhythmia detection. These optimization techniques help to fine-tune model parameters efficiently and enhance diagnostic reliability. Finally, the conclusion summarizes and explains the research path and future work in this field.