This work investigates novel methods for link prediction in graph-based data in order to solve the problem of future or absent connection detection in intricate networks. Graph architectures help to capture interactions among entities in many fields including social media, biological systems, and information technology. This work develops a complete framework for network analysis by integrating sophisticated machine learning algorithms and graph neural networks with classical graph theory measurements including common neighbors, Jaccard coefficient, and Adamic-Adar index. To capture local and global network features, the proposed method blends deep learning methods with heuristic similarity measurements. While unsupervised feature extraction enhances the representation of nodes and edges, a supervised learning approach is used, training the model using labeled data. Emphasizing the important functions of feature selection and parameter tweaking, extensive experiments on many datasets show trade-offs between model interpretability and predictive accuracy. Results show that hybrid techniques increase prediction results and dependability well above conventional methods. With possible use in recommendation systems, fraud detection, and biological interaction analysis, the results advance knowledge of network evolution and dynamics. With constantly strong performance, this work increases state-of-the-art link prediction and offers a road map for next research in dynamic graph analysis.