In the world of data, there is an urgent need to find ways to extract knowledge and information for improving patient care. Artificial intelligence (AI) is an emerging tool that has the potential to provide cardiologists with new insights and knowledge. The healthcare industry has already begun digital transformation for vast reams of data (Big Data) that are generated in routine clinical practice. AI has the potential to make a significant impact on healthcare by improving the efficiency of clinical care, providing personalized treatment and identifying new disease biomarkers. Machine learning (ML) and deep learning (DL) are AI techniques that utilize large data sets and computational power for analysis and decision making. There are 3 main ML techniques: supervised learning, unsupervised learning and reinforcement learning. Another functional AI service that has been presented is natural language processing (NLP) and it's applicable for analysing patient documentation. The scope of AI workflow, the most often used algorithms and performance metrics have been explained. The explainable artificial intelligence (XAI) has a prominent potential to be a useful tool for clinicians, as it provides full transparency into an AI model’s decision-making process and few applications were reviewed. The challenges and limitations of AI in cardiology have been discussed for both ethical, methodical and legal issues. Furthermore, the successful establishment of good practices towards the right development and deployment of automated ML-based systems will ensure a regulatory framework for strengthening the trust in AI/ML-based clinical decision support systems.