This systematic review examines the integration of Explainable Artificial Intelligence (XAI) methodologies within social sciences and humanities research, focusing on three principal approaches, feature attribution, counterfactual analysis, and model-agnostic visualization, and their application across diverse empirical domains. Feature attribution techniques, such as SHAP and LIME, have been adopted in archival text studies to quantify the contribution of individual lexical elements to topic model outputs, thereby elucidating latent thematic structures. Counterfactual analysis has proven instrumental in social media sentiment research, wherein minimally perturbed inputs expose classifier decision boundaries and reveal embedded biases. Model-agnostic visualization tools further enable scholars to interactively explore decision surfaces in network models of historical social structures, facilitating critical interrogation of community detection and relational dynamics. By synthesizing documented methodological workflows and available open-source toolkits, we identify best practices for harmonizing disciplinary expertise with computational frameworks, including guidelines for model selection, XAI implementation, and domain-expert validation. Evaluation metrics, explanation fidelity, coherence, and end-user interpretability are extracted from empirical studies to benchmark transparency and reproducibility. The proposed workflow begins with the articulation of a precise research question, proceeds through iterative model development and explanation generation, and culminates in collaborative validation with subject-matter experts. This integrated approach advances robust, accountable, and contextually informed computational inquiry, thereby fostering the maturation of XAI as an indispensable instrument in social sciences and humanities scholarship.