The sudden downtime and unplanned maintenance not only drastically increase the maintenance cost but also decreases the production capacity for the manufacturer industries. This is because the machines on these industries fail suddenly and totally stop the production as the machine should be fixed by maintenance before it can run again. To deal with it, several maintenance techniques have been adopted. But as soon as an automated maintenance technique comes in named predictive maintenance, the future machine failure can be predicted. To perform this prediction, a synthetic dataset is used that is taken from 100 industrial machines. From this dataset, the simulated sensor data, error, and failure history have used to calculate the probability of error and failure during the time period of an anomaly. This probability is calculated by the basic probability equation. In addition, the sum of the calculated probability of error and failure, give the intuition about the most relevant sensor data for a machine. This relevant sensor data is then used as response for the prediction with gaussian process regression algorithm. This prediction of response is shown for machine number 85 which is the most important from all 100 machines as this machine is very sensitive to any of the 4 sensor anomalies. Then, the sum of probability can be coherent with the anomaly on the predicted response which is the most relevant sensor data. This defines that the machine is in high risk of experiencing machine failure and thus the machine should be fixed by adopting maintenance. In contrast, the opposite is also true for low probability of error and failure for an anomaly on the predicted response. To evaluate the performance of the algorithm, four statistical metrics are used among which three matric is to estimate the errors and the other one is the correlation coefficient between the actual and predicted data.