Emerging trends and strategic opportunities in tiny machine learning: A comprehensive thematic analysis

Abstract

This study comprehensively reviews 779 Scopus-indexed documents, critically analyzing the trends, challenges, and opportunities in Tiny Machine Learning (TinyML). Through thematic analysis, 21 key themes are identified, covering areas such as edge computing, deep learning, IoT integration, microcontroller efficiency, and energy utilization. Unlike previous reviews focusing on specific domains or applications, this study adopts a text mining-based thematic approach to identify cross-sector research patterns and uncover underexplored areas. This positions the review as a broad yet deep mapping of the TinyML landscape. By synthesizing insights from the literature, this research highlights strategic opportunities, future directions, and technological advancements necessary to expand the application of TinyML in resource-constrained environments, neural networks, and real-time systems. The review also identifies key challenges such as balancing accuracy and energy efficiency in low-power devices, optimizing on-device learning, and ensuring data privacy without cloud dependency. In doing so, it outlines actionable directions for future research, including scalable deployment in large-scale IoT systems and application expansion into areas such as UAV security and smart cities. The findings are crucial for advancing AI applications in low-power, embedded systems and contribute to the growing body of knowledge on TinyML.