Cardiac diseases (CDs) are one of the leading causes of the growing global mortality rate. Early detection of CDs is necessary to avoid a high increase in the mortality rate. Machine learning-based computer-aided diagnosis of CDs using various physiological signals has recently been used by researchers. Since Pulse Plethysmograph (PuPG) signal contains a wealth of information about cardiac pathologies, therefore, this paper presents an expert system design for the automatic diagnosis of cardiac disorders like Hypertension, Dilated Cardiomyopathy and Myocardial Infarction using a novel fingertip PuPG signal analysis. The proposed system first performs signal de-noising of raw PuPG sensor data using Discrete Wavelet Transform (DWT). After signal segmentation, it extracts discriminant and simplest timedomain features, which are used to perform the detection of normal and abnormal subjects through a support vector machine (SVM) classifier. The proposed detection and classification systems are tested using 10-fold cross-validation which yielded an average accuracy of 98.90%, sensitivity of 100.00%, and specificity of 98.02% for detection (normal vs abnormal) experiments with only four features and an average accuracy of 97.57% for the multiclass problem using five computationally inexpensive features. Comparative analysis with existing methods based on Electrocardiogram, Photoplethysmograph, and Phonocardiogram revealed that the proposed system has high efficiency in terms of CD detection with very low computational complexity. The findings of this work provide insights into the contribution of PuPG signal analysis towards accurate detection of cardiac disorders through innovative, low cost, and non-invasive methods. Such a system with mobile cardiac health monitoring could be used as a counterpart or second opinion with clinical diagnoses and provide patients with additional but subtle indicators of varying heart dynamics.