Food spoilage, which causes 40–50% of all losses of root crops, fruits, and vegetables each year, is one of the biggest problems the world is currently experiencing. If the freshness or deterioration of a fruit can be determined before it is lost, the fruit waste problem may be mitigated. The goal of this work is to develop a simple model for tracking fruit quality using sensors and machine learning (ML). This model assists in determining the fruits that will ripen and require use earlier from the gases emitted by it. Two gas sensors (MQ3 & MQ7) and an Arduino Uno serve as the main processing components of the suggested system. Principal Component study (PCA) is a widely employed discriminating approach that has been utilized to differentiate between fresh and rotten apples based on sensed data. The study yields a cumulative variance of 99.1% over a span of one week. The data has also been evaluated using a linear Support vector machine (SVM) classifier, which has achieved an accuracy of 99.96%. The distinctive feature of the system is that it evaluates the levels of spoilage based on real-time data and deploys a low-cost, straightforward system that can be used anywhere to preserve any type of fruit.