Cortisol is a steroid hormone that regulates a wide range of vital signs throughout the body. However, current cortisol monitoring methods are inconvenient for everyday settings. Heart Rate (HR) and Heart Rate Variability (HRV) are easily collected biological parameters whose fluctuations highly correlate with cortisol, however, there does not exist a work attempting to estimate cortisol levels using these signals. In this paper, to the best of our knowledge, for the first time, we propose a machine learning-based salivary cortisol level estimation method using HR and HRV collected from pregnant women wearing an ECG chest strap. We first extract HR and HRV parameters from inter-beat-interval data derived from electrocardiogram signals. Then, we apply a feature selection algorithm to select the most contributing features and introduce a machine learning-based weak supervision method to address the unbalanced number of labels collected in real settings. Five machine learning algorithms are implemented to perform binary classification of baseline cortisol level (BL) versus two distinct cortisol levels (CL1 and CL2). One deep neural network is used to perform the classification across all three levels. As a pioneer study, we obtain prediction accuracy of up to 69% (BL VS. CL1), 71% (BL VS. CL2), and 60% (BL VS. CL1 VS. CL2).