A non-invasive system for detection of coronary artery disease (CAD) in a person, the system comprises: a plurality of physiological sensors for capturing a plurality of physiological signals from the person; a memory; and a processor in communication with the memory, the processor further comprises: a signal processing module processing the plurality of physiological signals to remove a plurality of noises; a feature extraction module for extracting time domain features, frequency domain features, time-frequency domain features and statistical features from each of the processed physiological signals; a classification module for classifying the person from each of the features independently as CAD or normal using physiological signal classifiers, wherein the classification is done using a supervised machine learning technique; a fusion module for fusing the output of the physiological signal classifiers; a detection module for detecting the presence of the coronary artery disease in the person using the physiological signal classifiers based on a predefined criteria, wherein the predefined criteria comprises choosing a reliable classifier if there is a classification mismatch between the outputs of the physiological signal classifiers, and wherein a reliable classifier is chosen based on an outcome of a classifier which has a highest probability score out of the each of the physiological signal classifiers; a plurality of physiological sensors for capturing a plurality of physiological signals from the person; a memory; and a processor in communication with the memory, the processor further comprises: a signal processing module processing the plurality of physiological signals to remove a plurality of noises; a feature extraction module for extracting time domain features, frequency domain features, time-frequency domain features and statistical features from each of the processed physiological signals; a classification module for classifying the person from each of the features independently as CAD or normal using physiological signal classifiers, wherein the classification is done using a supervised machine learning technique; a fusion module for fusing the output of the physiological signal classifiers; a detection module for detecting the presence of the coronary artery disease in the person using the physiological signal classifiers based on a predefined criteria, wherein the predefined criteria comprises choosing a reliable classifier if there is a classification mismatch between the outputs of the physiological signal classifiers, and wherein a reliable classifier is chosen based on an outcome of a classifier which has a highest probability score out of the each of the physiological signal classifiers; a signal processing module processing the plurality of physiological signals to remove a plurality of noises; a feature extraction module for extracting time domain features, frequency domain features, time-frequency domain features and statistical features from each of the processed physiological signals; a classification module for classifying the person from each of the features independently as CAD or normal using physiological signal classifiers, wherein the classification is done using a supervised machine learning technique; a fusion module for fusing the output of the physiological signal classifiers; a detection module for detecting the presence of the coronary artery disease in the person using the physiological signal classifiers based on a predefined criteria, wherein the predefined criteria comprises choosing a reliable classifier if there is a classification mismatch between the outputs of the physiological signal classifiers, and wherein a reliable classifier is chosen based on an outcome of a classifier which has a highest probability score out of the each of the physiological signal classifiers; <EOS>