Cardiopulmonary diseases are the primary reason behind worldwide mortality. Sudden and abnormal changes in heart rate (HR), respiration rate (RR), and blood pressure (BP) are some of the primary indicators of these diseases. Unexpected irregularity in heart rate (HR), respiration rate (RR), systolic (SBP), and diastolic blood pressure (DBP) are the primary indicators of physiological instability, including chronic cardiopulmonary diseases. Hence, monitoring these parameters regularly in an unobtrusive manner is crucial so they can also be used in the home environment. However, no prior study has been found that extracted all these parameters simultaneously from PPG. In this paper, we have proposed a first-of-its-kind deep learning framework, ‘VPE-Net', for estimating RR, HR, and BP (both systolic and diastolic) simultaneously from PPG without extracting any feature manually. The proposed model is designed by incorporating the framework of both gated and long-short-term recurrent convolutional networks and yields mean absolute error of 1.20 breath rate per minute, 1.44 beats per minute, 0.95 mmHg, and 0.77 mmHg for RR, HR, SBP, and DBP respectively.