Image forgery detection is a critical task in digital forensics, aimed at identifying and analyzing manipulations in digital images and videos. In this study, we present a novel hybrid approach that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Local Binary Patterns (LBPs) for image forgery detection. Our objective is to develop a robust and accurate system capable of identifying various forms of image manipulation, ranging from simple alterations to sophisticated forgeries. The CNN module identifies hierarchical features from images, effectively capturing both low-level textures and high-level semantics. Complementing this, the RNN component adds a temporal dimension to the analysis, enabling the detection of sequential patterns and dynamic alterations in videos. Meanwhile, the inclusion of LBPs enhances our model with a powerful texture descriptor, capturing fine-grained details indicative of forgery. Through extensive experimentation and validation, our hybrid model achieves an impressive accuracy rate of 95.2% in discerning between authentic and manipulated content. Looking ahead, we identify several avenues for future research, including model refinement through advanced architectures and optimization techniques, data augmentation to enhance robustness, exploration of adversarial defense mechanisms, optimization for real-time deployment, tailoring for domain-specific applications, and consideration of ethical and legal implications. Our research contributes to the advancement of image forgery detection technologies, with implications for various domains including law enforcement, media forensics, and content authentication.