Over the past decades, machine learning has become one of the significant solutions in extracting and analyzing useful information from large-scale data. Machine learning models have been increasingly used in various fields such as the medical, industrial, automation, and environmental assessment domains. In addition to the field of data mining, the different Internet of Things (IoT), cloud computing, fog computing, and mobile cloud computing fields have adopted machine learning techniques to overcome several shortcomings in their decision-making process. Automated machine learning-based decision-making greatly supports the numerous environment with the increased time efficiency and user satisfaction. Although, machine learning models encounter several difficulties in handling the raw data and continuously changing data. Hence, traditional machine learning models have been enhanced by integrating the different statistical or machine learning models to improve the quality of decision-making. Text classification, sentiment polarity, product or service recommendation, weather forecasting, stock price prediction, disease classification, attack detection, and spam classification are some of the emerging popular machine learning research areas.