In this golden age of rapid development surgeons realized that AI could contribute to healthcare in all aspects, especially in surgery. The aim of the study will incorporate the use of Convolutional Neural Network and Constrained Local Models (CNN-CLM) which can make improvement for the assessment of Laparoscopic Cholecystectomy (LC) surgery not only bring opportunities for surgery but also bring challenges on the way forward by using the edge cutting technology. The problem with the current method of surgery is the lack of safety and specific complications and problems associated with safety in each laparoscopic cholecystectomy procedure. When CLM is utilize into CNN models, it is effective at predicting time series tasks like identifying the sequence of events in the Laparoscopic Cholecystectomy (LC). This study will contribute to show the effectiveness of CNN-CLM approach on laparoscopic cholecystectomy, which will frequently focus on surgical computer vision analysis of surgical safety and related applications. The method of study is deep learning based CNN-CLM to better detect nominal safety as well as unsafe practices around the critical view of safety and AI-based grading scale. The general design flow of AI-recognition of surgical safety is firstly collecting safety surgical videos for frame segmenting and phase according to the image context by surgeon reviewer by CNN-CLM. For this advance research, the dataset is splatted into three main parts where 70% of which is used for training, 15% of which is used for testing and the rest for the cross validation, to achieve the accuracy up to 98.79% of this specific research.  For result part, different metrics of CNN-CLM to evaluate the performance of the proposed model of safety in surgery. The study uses one of the top three performing methods CNN-CLM for the evaluation yields and anatomical structures in laparoscopic cholecystectomy surgery.