This paper introduces BioFusionNet, a groundbreaking deepfake detection framework that cleverly combines spatial, temporal, and physiological features like skin color variations, micro-expressions, involuntary facial muscle movements, blinking patterns, pulse signals through a multi-cue ensemble learning approach. Unlike traditional methods that mainly focus on visual artifacts and timing inconsistencies, BioFusionNet features a specialized function named Physiological Branch (PB) aimed at capturing subtle biological signals that generative models often struggle to mimic. By utilizing an attention-based fusion mechanism, our model skillfully merges the strengths of convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) branches with these physiological cues, creating a highly effective detection system that excels even with limited training data. Our experimental results show marked improvements in the detection accuracy, precision, recall, and overall robustness of the model, setting the stage for more reliable deep-fake forensic tools in real-world scenarios.