Smart manufacturing, additive manufacturing, civil engineering, biomedical science, and other sectors now use machine learning, which has evolved from a laboratory need to a vital procedure. The main concept of machine learning involves exploring prospective algorithms for solving existing challenges, as well as investigating both shallow and deep learning methods to monitor processing variables in additive manufacturing. Identifying the relationships between variables used as input and output in additive manufacturing products is crucial for producing superior parts, irrespective of the manufacturing algorithm, user input, and output. The type of material, defect type, ML algorithm, and type of input data for different additive manufacturing technologies are described to provide a methodical and thorough summary.