In the artificial intelligence domain, human action recognition (HAR) has evolved as one of the major active research topics as a reason of diverse applications, namely video surveillance. The extensive variation types amidst routine human activities make the recognition procedure much more intricate. As a reason for an increment in the cameras’ usage, automated systems are essential to categorize activities, such as these utilizing computationally intelligent methodologies, namely machine learning and deep learning (DL). This paper’s organization covers all the characteristics of the HAR’s general framework. This work talks about the public dataset’s characteristics utilized that are aimed at HAR. After that, it summarizes and classifies the recently published research enhancement under the common framework. The merits and demerits of dimensionality reduction, action representation, and as well as action analysis methodologies are offered. After that, the outcomes exhibit the DL methodologies’ performance via graphs. This article gives a clear vision of what we can explore in the activity recognition area. The work talks about HAR’s challenges and its future directions.