The Industry 4.0 technology relies on Single-board computers and the Internet of Things (IOT) and Machine Learning (ML). In addition, sensory detectors, controllers, and a communication interface are employed to address the demands of distant supervision and operational management. In today's industrial environment, understanding machines and giving effective interpretation and prognostics is a challenging issue. This paper presents an effective on-process identification tool for monitoring and advising the operator based on sensor parameters. The data is analysed with the Fast Fourier Transform (FFT) inference and machine learning strategies to detect the production calibre of an industrial Computer Numerical Control (CNC) machine, including vibration, temperature, humidity, and operating temperatures. The vibration parameter is provided to the FFT algorithm to produce frequency, and the diameter dataset is also provided manually from the hole diameter in the job piece to correctly monitor and inspect the product quality to prognostics to the fault in machines. Improper machine settings cause varied vibrations and changes in parameters, whereas our Industry 4.0 module detects and warns about faulty parameters. The device is put through its trials using three distinct machine learning approaches, and the results are collected. ML combines the outcomes of many baseline estimators to provide better results. This research work utilizes the Linear regression model since it has a high-power detection ability and minimizes variance as well as bias. The Single board computer using FFT and linear regression monitoring gives greater data accuracy of 97.6 percent on comparing the outcomes of 5.4%improved efficiency than K-Nearest Neighbourhood (KNN), 95.5% Random Forest Network (RN), and 95% Support Vector Machine (SVM) algorithms. The proposed system was validated via the deployment of suitable test scenarios, illustrating the technique's effectiveness in manufacturing environments.