The research delves into the integration of colorimetric sensors in detecting volatile organic compounds (VOCs) for rapid bacterial identification through advanced machine-learning algorithms. With the use of a colorimetric sensor array that detects any VOCs in the form of a chemical change, we were able to establish a methodology. The pattern was formed and further deep analysis of this pattern to produce homogeneity in results was the goal. This method uses optoelectronic arrays to process RGB data, allowing for highly specific bacterial sample separation. Artificial intelligence frameworks are used in the creation and testing to improve detection capabilities and increase accuracy even with little data. The final ANN model utilized for the image classification was able to produce 92% accuracy within 2 minutes after utilizing a training sample of 235 samples and testing it on 10% of data throughout the span of 2 months. The results of the findings extend to clinical diagnostics, where accurate detection might facilitate targeted treatments and expedite pathogen identification. The results indicate potential for practical application, providing a robust tool for non-invasive bacterial classification.