The Industry 4.0 rose and brought forth unrivaled technological combinations, where the industrial era is greatly shaped by artificial intelligence (AI). Although, there opacity issues inherited in AI systems pose a few challenges. To overcome this, Explainable (XAI) emerges as a solution for all, giving understandable justifications for AI systems through various techniques and interpretable models, that represent a transformative change toward creating transparent, understandable, and accountable AI systems. By using XAI, the space created between different complex problems to human understandable solutions is enhanced over transparency and trust towards AI decisions and processes, and this helps stakeholders to navigate and maintain their transformational era, innovations, and sustainability over various sectors. This chapter focuses on the basic principles of XAI and their relevant nature for enhancing decision-making processes in Industry 4.0. As we know, there is a huge increase in AI over industrial applications—ranging from predictive maintenance to complex automation and supply chain optimization, the need for explainability became most important to ensure trust and compliance with regulatory standards. XAI provides in depth conceptualization of the AI-driven decision making by elucidating the reason behind model outcomes, which in turn aids in debugging and improvising the models, fostering trust among the users. The principles of XAI’s transparency, interpretability, fairness, and accountability are clear, focusing on their implementation in industrial applications. The chapter concludes with strategic recommendations for integrating XAI principles effectively into Industry 4.0 initiatives to leverage AI’s full potential by maintaining ethical and operational standards.