Social science research increasingly relies on sophisticated analytical tools to uncover complex, nonlinear patterns in large-scale and heterogeneous datasets. This review explores the intersection of artificial intelligence (AI) and nonparametric statistical estimation within the social sciences. Emphasis is placed on machine learning (ML) and deep learning (DL) methods that extend classical statistical techniques, enabling researchers to analyze social phenomena without imposing rigid parametric assumptions. Methodological innovations—such as deep kernel learning, reinforcement learning for model tuning, and neural additive models—are examined for their application in areas like public opinion modeling, income inequality, educational outcomes, and behavioral prediction. We also discuss theoretical developments, including consistency, convergence, and generalization, that support the integration of AI into nonparametric frameworks. Challenges such as interpretability versus accuracy, computational costs, and ethical considerations are addressed. We conclude by outlining future research directions, including hybrid modeling, fairness-aware inference, and privacy-preserving analytics in social data contexts.