This chapter delves into the utilization of artificial intelligence (AI) algorithms and methods in the context of transmission line fault detection. Power supply disruptions of considerable magnitude can result from faults in transmission lines, thereby endangering the dependability and stability of electrical infrastructures. Conventional approaches to fault detection exhibit deficiencies in precision and speed of response, thereby diminishing their efficacy when confronted with the intricacies and ever-changing characteristics of transmission line faults. Chapter “Smart Intelligence Perspective and Integrating AI with the Power System” commences with a comprehensive exposition on transmission line faults, encompassing their origins, prospective ramifications, and causes. This emphasizes the necessity for sophisticated defect detection methods in order to optimize the functionality of power systems. The subsequent section discusses the function of AI in fault detection, highlighting its capacity to process vast quantities of data, adjust to dynamic environments, and discern nuanced fault patterns. The subsequent section delves into various AI-driven techniques employed in the realm of defect detection. An in-depth examination is conducted of machine learning algorithms, including support vector machines, decision trees, and neural networks. The aforementioned models’ capacity to acquire knowledge from past data and generate precise prognostications is deliberated, in addition to the difficulties associated with their training and optimization. In addition, the chapter explores various deep learning methodologies, such as recurrent neural networks and convolutional neural networks, which have demonstrated encouraging outcomes in the domain of defect detection. By automating feature extraction and capturing temporal dependencies in fault patterns, these methods improve the precision of fault detection systems. Furthermore, an analysis is conducted on the implementation of sophisticated optimization algorithms, including particle swarm optimization and genetic algorithms. Algorithms of this nature facilitate the optimization of fault detection models, the selection of pertinent features, and the enhancement of the fault detection process’s overall efficiency and performance. An investigation is conducted into the potential benefits of integrating AI techniques with other intelligent systems, including data analytics platforms and Internet of Things (IoT) devices, in order to improve fault detection capabilities and leverage real-time data. In addition, the chapter addresses the obstacles and factors to be taken into account when deploying AI-driven fault detection systems. These encompass the accessibility of data, the interpretability of models, and the computational demands. Furthermore, this paper showcases case studies and practical implementations of defect detection in transmission lines utilizing AI. The presented case studies exemplify the effective implementation of artificial intelligence (AI) methodologies in practical situations, thereby demonstrating their capacity to enhance the resilience of power systems, decrease response time, and improve the accuracy of fault detection. Subsequently, the chapter culminates in an examination of the emergent trends and future potential of AI-powered fault detection. This study investigates the feasibility of incorporating sophisticated artificial intelligence algorithms with nascent technologies such as edge computing, 5G networks, and distributed sensing systems. By doing so, it enables the development of transmission line defect detection solutions that are both intelligent and resilient. In conclusion, this chapter presents a thorough examination of algorithms and methods utilizing artificial intelligence to identify faults in transmission lines. It investigates a variety of AI techniques, their advantages and disadvantages, as well as their practical applications and future directions. The purpose of the insights presented in this chapter is to motivate practitioners, industry experts, and researchers to make additional progress in the domain of defect detection utilizing AI as it pertains to transmission line systems.