The present study explores insights into the applicability of Explainable Artificial Intelligence (XAI) technologies for reshaping mental health care. The objective is to estimate the effect of XAI on various major mental health disorders, the novel machine learning (ML) techniques employed, and the datasets utilized. The study is done using a scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, examined relevant literature from the past decade (2020–2024), and identified 20 studies for our analysis in which XAI is applied to diverse mental health conditions. Specifically, these papers are selected based on identifying the novel techniques and datasets influencing the mental health domain. The review finds that the most commonly used datasets are EEG and MRI, and the prevalently employed XAI technique is SHAP (Shapley Additive Explanations) to dissect various factors influencing mental health outcomes.