In this paper, we propose an innovative approach toward identifying deepfakes, an emerging digital media concern. The synergy of Convolutional Neural Networks (CNNs), Ant Colony Optimization (ACO), and Grad-CAM is used in the proposed method that exhibits an excellent potential for identifying deepfakes with an effectiveness rate of 96%, an improvement over prior art methods. Our findings corroborate the effectiveness of this holistic method as well as its applicability in social media platforms, content filtering, and law enforcement. Based on prior art, this paper extends the realm of deep learning into the area of detecting forged content in various forms such as images and video. Through the amalgamation of the strengths of various deep learning paradigms, our approach successfully detects complex deepfakes that more conventional methods of detection might not detect. Our empirical findings validate the effectiveness of our work and its potential for combating the emerging menace of deepfakes in digital media.