Abstract
Outdoor ceramic insulators are critical components of power transmission and distribution overhead lines, providing essential mechanical support and electrical insulation for high-voltage conductors. Their reliable operation is increasingly challenged by material aging, physical defects, and surface contamination, which can lead to flashover events and subsequent power outages. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful, scalable, and data-driven tools for automated condition assessment of outdoor ceramic insulators, offering faster, more objective, and more consistent alternatives to traditional manual inspection practices. This paper presents a comprehensive and structured review of state-of-the-art ML/DL-based approaches for the diagnosis and condition assessment of outdoor ceramic insulators. The review contributes by organizing the field along three complementary dimensions: (i) foundational ML/DL methodologies and their adaptation to non intrusive insulator monitoring; (ii) comparative analysis of image and radiation based sensing modalities, including infrared, ultraviolet, acoustic, and radio frequency techniques, together with multimodal data fusion strategies; and (iii) a critical examination of dataset limitations, validation practices, and common misconceptions such as overfitting and insufficient field evaluation that explain contradictory results across studies. Emerging trends, including unmanned aerial vehicle (UAV)-based inspection, synthetic data generation, and autonomous intelligent monitoring, are synthesized to identify open challenges and best practices toward reliable, interpretable, and scalable artificial intelligence (AI)-driven inspection systems.