Edible oil is an essential ingredient in many cuisines, but it can also be adulterated or degraded by various factors. Therefore, it is important to develop reliable and rapid methods for edible oil analysis. The objective of this paper is to propose a novel approach for classifying edible oil samples based on Near-Infrared Spectroscopy (NIR) and Electronic Nose (E-nose) data using Case-Based Reasoning (CBR). NIR and E-nose are non-destructive and portable techniques that can capture the chemical and sensory properties of edible oil. CBR is a machine learning method that can solve new problems by retrieving and reusing the most similar past cases. This paper describes the data collection, pre-processing, feature reduction, and CBR implementation steps. The performance of the CBR system was evaluated using a confusion matrix. Three samples of edible oils were classified: fresh cooking oil, adulterated cooking oil, and fresh olive oil. The results show that the proposed method achieves a 100% classification accuracy, demonstrating the effectiveness and efficiency of integrating NIR, E-nose, and CBR for edible oil analysis. The paper concludes that the proposed method can be applied to various types of edible oil and suggests some future directions for improving the system.