Hard disk failure occurs due to the malfunctioning in the operation of configured computers. Due to some of the external factors such as natural calamities, electrical disturbance can cause impact over the failure of a hard disk. Data corruption, distortion and disruption in the computer’s hard drive due to the malware infection in the disk. If the hard disk fails, the PC can stop its function by deriving any noise. Due to the hard disk failure, the system halts the process due to the triggered physical failure. As the data centers are predominantly increasing, the hard disk space complexity plays a vital role. Continuous monitoring of the computer can reduce the efficiency of the lack of control over the security aspects. In this paper, the artificial intelligence with machine learning is used to predict the potential features in the disk. Detection of the hard disk features by determining the attributes with the statistical tests of compatibility. Different methods are used to analyze the test parameters using the svm, random forest and naïve bayes classifier to analyze the accuracy over the approximation results. Prediction of failure test can improve the reliability in the storage system. Different techniques are used to analyze the signs of the failure. The warning signs are detected to identify the early detection of the failure in the hard disk. Some of the viruses or malware infected files can destroy the data in the hard disk, which can cause potential data losses. A hard drive crash can fail up the boot process where it gets corrupted. Some of the corruptions are firmware, ransom ware corruption which causes unreadable which leads to the data loss. Electronic failure or power surge can cause the system to halt process. The internal failure such as corruption of files, overheating and external factors such as human errors can cause permanent damage to the data loss medium. The AI is used to predict the failure over the hard drive model for identifying the exact precision. An external hard drive has to be checked monitoring based upon the failure statistical report. In some data centers, the HDD will overheat due to the high consumption of energy and overheating due to the maximum range. So, to avoid such disruption, the failure has to be analyzed by using AI with machine learning. The comparative results are analyzed to predict the HDD failure using AI and MI methodologies.

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