In the current digital age, the proliferation of deepfake technology and image forgery present significant challenges across various sectors. Techniques like splicing, copy-move forgery, and deepfake generation have been increasingly used to deceive audiences by spreading false information and rumors causing security risks, privacy concerns and victimization. This work aims to contribute to the advancement of forgery detection technologies by enhancing media security and promoting trust in digital content. It proposes a deep learning-based approach for detecting deepfakes, leveraging the power of Convolutional Neural Networks (CNNs) to enhance image forgery detection. The proposed model was trained and tested on a publicly available dataset comprising both real and forged images, ensuring comprehensive evaluation across various types of manipulations. The model proposed in this study is compared on the basis of the classification metrics, ROC-AUC curve and testing. The key contributions of this work include the development of a simple yet highly effective architecture for deepfake detection, which outperforms traditional detection methods. The results of our study show that the proposed approach achieves a promising detection rate, high accuracy in distinguishing between authentic and fake images, offering an efficient and scalable solution to counteract the growing threat of deepfake technology.