Abstract: Ice coating on transmission lines poses the greatest threat to the safe and stable operation of power grids in severe cold and high-altitude mountainous regions, often leading to issues such as broken conductors, tower collapses, and line tripping, which severely impact power supply. Traditional ice detection relies on manual inspections, exhibiting drawbacks like time-consuming processes, low accuracy, and high risks. De-icing measures are also predominantly manual or passive, resulting in low automation levels. Based on engineering application cases, this study investigates key technical issues in transmission line icing monitoring and intelligent de-icing from a deep learning perspective. It primarily explores precise icing parameter identification algorithms, multi-source information integrated monitoring methods, and intelligent de-icing control strategies compatible with monitoring devices. Experimental data analysis is used to validate the technology’s effectiveness, while practical operational improvement suggestions are provided. The study offers a rapid, accurate, and cost-effective technical solution for transmission line anti-icing, enhancing the smart grid’s ice-resistant disaster response capabilities.