Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.