The machine learning-based framework presented proves to be a highly effective and influential tool for both the analysis and prediction of linkages within social media platforms. By utilizing cutting-edge techniques, the framework performs exceptionally well in examining current ties as well as predicting future connections, which adds to a more sophisticated comprehension of the complex web of connections seen in social networks. Central to its proficiency is the utilization of machine learning techniques, particularly the Dynamic Contextual Link Prediction (DCLP) model, renowned for its adeptness in capturing dynamic contextual information and temporal relationships. A thorough performance test using several machine learning models—including the DCLP model, Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression—highlights the superior capabilities of DCLP. Recall of 93.74%, accuracy of 92.14%, precision of 94.52%, and an astounding F-score of 96.35% are among the noteworthy measures. These results affirm the DCLP model’s effectiveness in accurately predicting and classifying outcomes, positioning it as a robust and high-performing solution. The findings highlight that DCLP outperforms traditional machine learning models, emphasizing its proficiency in capturing dynamic contextual information and temporal dependencies to enhance predictive accuracy within the scope of the examined task.