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Determining and proving that a specific person or several persons may or may not be there at the Crime Scene (CS) in every criminal investigation are vital. Thus, in the law enforcement community, more often the physical evidence is collected, preserved, and analyzed. The accused cannot be predicted by normal people or judge just by looking at the evidence obtained at the analysis phase. So, research studies were undertaken on automated recognition as well as retrieval system aimed at forensic Crime Scene Investigation (CSI). A survey on the investigation of forensic CS evidence is depicted here. The main focus is rendered on the computer-centered automated investigation system. The latest research on the different evidence-centered Forensic Investigation (FI), such as the face, Finger-Print (FP), shoeprint, together with other Foot-Wear (FW) impressions, Machine Learning (ML) algorithm-centered FI, ML-centered pattern recognition, features of disparate evidence in forensic CSI, and various matching technique-centered FI, is surveyed here. Finally, centered on the accuracy and other two metrics, the methods' performance for CSI is compared. Out of all the other methods, OLBP + LSSVM produced better results for precision and recall followed by CLSTM. In terms of accuracy, CLSTM produced better results than any other method. [ABSTRACT FROM AUTHOR]