The COVID-19 (Corona virus disease 2019) outbreak induced millions of human beings the loss of life, still spread of the disease was unstoppable and was declared a pandemic with the aid of the WHO (World Health Organization). Therefore, human beings across the world are nevertheless getting contaminated every day. Reverse Transcription Polymerase Chain Reaction (RT-PCR) to identify COVID-19 is not feasible in terms of both cost and time of identification, which might cost the patient's life. Therefore, to make identification economical and feasible, researchers were attempting to use clinical images (x-ray and CT etc.,) to detect COVID-19 with the assist of Machine Learning (ML) and Artificial Intelligence (AI) approaches to aid in automating the identification of the pandemic. This paper helps in understanding some ML and AI approaches for detecting COVID-19 from clinical lung images. The amassed records about accessible research sources and inspected a complete of 30 research papers until august 2021. This paper includes the exploration and analysis of data sets, pre-processing methods, segmentation approaches, feature extraction, classification, and test effects, which can be useful for discovering future lookup instructions in the area of automated analysis of COVID-19 sickness the usage of AI-based frameworks. [ABSTRACT FROM AUTHOR]