Multimedia forensics has become a crucial field in ensuring the authenticity and integrity of digital content. With the rapid advancement of artificial intelligence (AI) and machine learning (ML), this domain has evolved to leverage sophisticated techniques for analyzing, verifying, and detecting anomalies in multimedia data. This research introduces innovative AI and machine learning (ML)-driven approaches to address key challenges, including deepfake detection, provenance analysis, and forgery identification. We propose a comprehensive framework that integrates deep neural networks (DNNs), adversarial learning, and graph-based methodologies to improve the detection and classification of manipulated media. Using convolutional neural networks (CNNs) and transformer architectures, our approach effectively identifies pixel-level inconsistencies and temporal artifacts in images and videos with high precision. In addition, we introduce a novel interpretability module to enhance transparency and reliability in forensic decision-making. Our methodology is validated using publicly available datasets such as FaceForensics++, DFDC, and real-world anomaly detection datasets. Preliminary experimental results indicate significant improvements in detection performance, achieving an average precision of 94.6% and a recall of 92.1%, surpassing existing state-of-the-art methods. Also, we explore the practical implications of these advancements in real-world applications, including law enforcement, digital rights management, and cybersecurity. By bridging the gap between theoretical advancements and real-world applications, this research advances the role of AI and ML in multimedia forensics. Future work will explore the integration of quantum computing paradigms and blockchain-based traceability systems to further enhance the resilience and reliability of forensic methodologies.