The increasing integration of artificial intelligence (AI) into various domains necessitates an understanding of its decision-making processes to ensure reliability and ethical use. This paper addresses the “black box” problem, where the opacity of AI models impedes trust and accountability. Objective: To enhance the transparency and comprehensibility of AI systems, particularly through explainable artificial intelligence (XAI). Methodology: It includes a detailed examination of current XAI techniques such as Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependency Plots (PDP), and decision trees, alongside an exploration of their limitations and potential improvements. Findings: It highlights that while these methods contribute significantly to model interpretability, they often fall short in accuracy and user comprehension. This research proposes hybrid approaches combining multiple XAI techniques to address these gaps. Conclusion: The study emphasizes the need for on-going development in XAI to foster responsible AI usage and paves the way for future advancements that can better balance transparency, privacy, and ethical considerations. This study underscores the critical role of XAI in enhancing the trustworthiness of AI, particularly in sensitive applications like healthcare, and outlines future research directions to overcome existing challenges. By addressing the limitations of current XAI methods and proposing improvements, this paper aims to promote responsible AI development, ultimately unlocking the full potential of AI for societal benefit.