Explainable Artificial Intelligence (XAI) has emerged as a critical paradigm in the evolution of data-driven decision-making systems, responding to growing concerns surrounding opacity, trust deficits, ethical accountability, and regulatory compliance in artificial intelligence deployments. As AI systems increasingly permeate high-stakes domains such as consumer-centric business environments, supply chains, e-commerce platforms, and healthcare systems, the need for transparency, interpretability, and human-centered understanding has become both a moral and operational imperative. This research article develops a comprehensive, theory-driven, and empirically grounded examination of XAI as a foundational mechanism for sustainable growth, organizational trust, and responsible innovation. Drawing strictly on established literature, this study synthesizes insights from business sustainability research, human–computer interaction theory, decision sciences, and biomedical informatics to construct an integrative framework explaining how XAI enables trust calibration, mitigates bias, enhances user acceptance, and supports regulatory alignment. The article further explores methodological approaches employed in empirical XAI research, including survey-based modeling, case study analysis, and system-level evaluation, emphasizing interpretability as both a technical and socio-cognitive construct. Findings from prior empirical studies are descriptively analyzed to demonstrate consistent relationships between explainability, perceived effectiveness, reduced discomfort, trust formation, and long-term adoption across domains. The discussion critically interrogates theoretical tensions, practical limitations, and contextual dependencies of XAI implementations, particularly in complex organizational and healthcare settings. Finally, the article articulates future research directions and policy implications, positioning XAI not merely as a technical add-on but as a transformative governance mechanism for ethical, sustainable, and human-aligned artificial intelligence systems.