Voice pathology detection is a critical area of research that leverages advancements in machine learning, deep learning, and artificial intelligence to improve diagnostic accuracy and early detection of voice disorders. The study presents a thorough analysis of the research landscape on voice pathology detection from 2006 to 2024, using data from Scopus. In total, 92 documents from 68 sources have been identified, and the literature review cycle was completed within articles, book chapters, and conference papers that gave substantial contributions to the field concerned. It identifies the key trends in the area under consideration, covering the shift from traditional diagnosis methods to sophisticated AI-based approaches. The field has experienced a 14.25% annual growth rate, with documents averaging 3.53 years old and 21.2 citations each. This growth is particularly evident in feature extraction and deep learning. Research activity has significantly increased since 2019, reflecting the rising adoption of AI and machine learning techniques. Collaboration is a major trend in the field, with 294 authors averaging 4.23 co-authors per document. Additionally, 29.35% of the work involves international collaboration. Ongoing international collaboration and development of foundational areas are crucial for sustaining innovation in detecting voice pathologies. These findings highlight the expanding collaborative nature of voice pathology research and identify key areas for further exploration, including the integration of AI applications into clinical practice and associated ethical considerations. This study aims to guide future advancements in AI-based voice pathology detection within healthcare systems.