Artificial Intelligence (AI) is rapidly transforming warehouse management by enabling automation, enhancing inventory accuracy, and supporting data-driven decision-making. This study provides a comprehensive systematic review of AI applications in warehouse management through a dual approach combining bibliometric and thematic analyses. Utilizing 178 peer-reviewed publications sourced from the Scopus database, bibliometric methods including co-citation, co-occurrence, and co-country analyses were conducted with VOSviewer and Tableau to map research trends, influential authors, and global collaboration networks. Subsequently, a focused thematic analysis was performed on a subset of 61 highly relevant articles to identify emerging themes and research gaps, supported by keyword co-occurrence mapping and qualitative coding. Findings reveal growing scholarly interest in dynamic publication patterns, concentrated contributions from leading countries such as the United States and China, and prominent research clusters centered on AI-driven automation, human-AI collaboration, data integration, and infrastructural challenges. The study highlights significant disparities in AI adoption between developed and emerging economies and underscores the need for localized solutions tailored to infrastructure and workforce contexts. This integrated analysis not only charts the intellectual landscape but also offers actionable insights to guide future research and practical implementation of AI in warehouse management, particularly within emerging markets.