Cracks are one of the significant criteria utilized for diagnosing the disintegration of solid structures. Normally, a structural designer with specific information would assess such structures by checking for breaks outwardly, outlining the aftereffects of the examination, and afterward getting ready to investigate information based on their discoveries. A review technique like this is not without a doubt, as it requires manual chronicle of upwards of a few hundred thousand splits individually. However, it additionally cannot precisely identify the length and state of the breaks. To handle the issue, the industry has progressively looked toward utilizing AI detection to conduct a direct based assessment. Researchers are as of now building up a mobile-based intelligence equipped with AI analytics for recognizing splits and different imperfections, which will upgrade the proficiency of the investigation process and enhance the emergency response towards users. This study aims to provide an insight on the most efficient crack detection method by comparing several techniques. It was found out that the hybrid technique was the most efficient method to detect cracks. This technique combines the positive points from both the artificial neural network (ANN) and artificial bee colony (ABC) to come up with a more proper entirely new method.