The chapter outlines the mathematical aspects of social intelligence and covers major estimation theory techniques such as Maximum Likelihood Estimation (MLE), Expectation-Maximization (EM), Hidden Markov Models (HMM), Bayesian Estimation, and Subjective Logic. With our understanding of their mathematics and application, we seek to shed light on their utility in parameter inference and uncertainty-based decision-making in social intelligence settings. Furthermore, the chapter transitions into an analysis of deep learning models such as Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformers, emphasizing their architectural innovations and application-specific optimizations. This comprehensive synthesis provides a unified framework for understanding and leveraging advanced computational methods in Social Intelligence, setting the stage for future research and applications in this interdisciplinary domain.