The article discusses the possibilities of using artificial intelligence (AI) methods to improve the inspection process of apartment buildings (AB) in the Arctic zone. Given the specific conditions of the region, such as extreme temperatures, wind loads and high humidity, traditional inspection methods are often insufficiently effective. The research aims to integrate computer vision (CV), natural language processing (NLP) and machine learning (ML) technologies to optimise the main stages of inspection: analysis of technical documentation, visual and instrumental inspection, measurement, verification calculations and recommendation generation. As a result, key combinations of AI methods have been identified that automate routine processes, increase the accuracy of defect diagnosis, improve the quality of decision-making and reduce the time required to conduct inspections. This helps to improve the reliability and durability of buildings, reduce operational risks and cut repair costs in the Arctic zone.