Deep learning models are pivotal in the advancements of Artificial Intelligence (AI) due to rapid learning and decision-making across various fields such as healthcare, finance, and technology. However, a harmful utilization of deep learning models poses a threat to public welfare, national security, and confidentiality. One such example is Deepfakes, which creates and modifies audiovisual data that humans cannot tell apart from the real ones. Due to the progression of deep learning models that produce manipulated data, accurately detecting and classifying deepfake data becomes a challenge. This paper presents a groundbreaking IRV2-Hardswish Framework for deepfake detection, leveraging a hybrid deep learning architecture that synergizes residual blocks in CNNs and the Inception-Resnet-v2 model. By incorporating residual blocks to capture underlying audiovisual data layers and enhancing Inception-Resnet-v2 with Hardswish activation for robust feature extraction, our framework achieves accurate detection of deepfakes. Furthermore, additional dense layers are integrated to ensure precise classification, establishing a comprehensive and effective solution for deepfake detection. Further, a detailed comparison of our framework with the state-of-the-art CNN models reports that our framework outperforms with 98% accuracy, 96% precision, and 95% AUC using the Deep Fake Detection Challenge (DFDC) dataset. The DFDC dataset is the largest, consisting of approximately 5,000 clips, including 1,132 actual and 4,118 false ones. The results report the efficiency of the proposed framework. These results demonstrate the framework's effectiveness in deepfake detection.