A unique approach based on developments in machine learning techniques has been developed to rapidly and accurately identify frequency anomalies in atomic clocks. The results of the tests show that compared to the innovation technique and the extrapolation approach, the innovative method has a higher detection potential for micro frequency flaws and a faster discovery time. The problems with Indian UTC have also been discussed. The continual addition of a leap second to UTC is a significant negative. A Cyber-Physical System (CPS) has been proposed for the national timing system to identify a difference in atomic clocks. There have also been extensive expressions of worry on the safety of CPS. We have also discussed time allocation and the CPS model. During the study, eight different atomic clocks were considered and compared to one another. High levels of accuracy and precision were found in the developed model, and it was suggested that the 7th and 8th clocks have values of 0.9310 and 0.9643, while the recall and f1-scores are higher with values of 0.9643 and 0.9643, respectively. This indicates that results will vary in the LSTM based ML models of anomaly detection as epochs and noise levels vary.