The pain measurement is a very vital aspect of healthcare, but the traditional approaches offer extensive dependence on manual feature extracting through physiological measurements and clinical judgement. These conventional methods involve expert knowledge, they are time consuming and have a tendency to fail to be generalized effectively nor across various groups of patients. To overcome these shortcomings, this paper comes up with a proposal of an automated pain recognition model that processes physiological signals through a hybrid deep learning model. The model combines Convolutional Neural networks, Bidirectional Long short term memory networks, and Gated Recurrent Units with which hierarchical spatial and time representations of raw signals are learned automatically. The architecture captures the local signal properties as well as the long-range contextual dependencies which enhance the difference between pain and no-pain states. Further, a stacking classifier ensemble is used to improve the prediction robustness and generally improve its performance. The proposed method has a high level of reliability as experimental assessment shows that this method is accurate at the level of 99. The system offers scalable and intelligent capability of objective pain monitoring which aids in supporting the making of better clinical decisions and patient care within the contemporary healthcare settings.