The integration of deep learning and fuzzy logic presents a promising advancement in pulse-based diagnosis, particularly within traditional medical systems such as Siddha and Ayurveda. This study proposes a novel Deep Fuzzy Framework for interpreting wrist pulse signals acquired through non-invasive sensors. Raw pulse waveforms are preprocessed and analyzed using convolutional neural networks (CNNs) to extract time-frequency and morphological features. These features are then mapped into a fuzzy inference system that emulates traditional diagnostic logic, including the assessment of Vata, Pitta, and Kapha imbalances. The hybrid system addresses the challenges of subjectivity and non-linearity inherent in pulse diagnosis by combining the precision of deep learning with the interpretability of fuzzy reasoning. Experimental evaluations demonstrate improved diagnostic accuracy and consistency over conventional machine learning approaches. This work offers a step toward intelligent decision support systems that bridge traditional knowledge and modern computational intelligence in personalized healthcare.