## 1 Introduction  
First defined in 2006 by the U.S. National Science Foundation (NSF), cyber-physical systems (CPS) are \"engineered systems that are built from, and depend upon, the seamless integration of computation and physical components\" [1]. The technological advancement of information and communications technologies (ICTs) vastly strengthens the coupling of the cyber and physical layers of many systems [2]. Among them are cyber-physical distribution systems (CPDSs), which integrate the primary components of the distribution systems and digital technologies. The CPDSs is an integral part of power systems to feed the electricity from transmission systems to communities and industries, significantly affecting decisions regarding the planning, monitoring, and coordination of the power grid [3]. The deployment of advanced metering infrastructure (AMI) integrated with the Internet of Things (IoT's) has transformed the traditional CPDSs. Smart meters, phasor measurement units (PMUs), smart appliances, and smart plugs enable the collection of various data [4], while advanced IoT architecture allows for fast and accurate data transmission for system monitoring [5]. Although these advancements have opened up opportunities to improve the reliability, security, and efficiency of the CPDS operation, numerous challenges have arisen. Traditional techniques might be ineffective in handling the collected big data, which can be characterized by high volume and dimension, diversity in variety, velocity, veracity, and values [6]. Furthermore, deep penetration of distributed energy resources (DERs) introduces new challenges and complexities, such as variations in voltages, power quality issues, uncertainties of DER generation, and bi-directional power flow [7]. More specially, distribution systems are distinguished from traditional transmission systemsdue to high r/x ratio, multi-phase unbalanced operation, limited sensor installation, poor observability, and frequent topology changes. Nonetheless, the traditional methods, most of which are originally developed for transmission systems, struggle to address such challenges [8]. As such, it is crucial to deploy techniques that are capable of processing, monitoring, and utilizing historical and real-time data of large volume, dimension, and heterogeneity. The COVID-19 pandemic has led to a surge in data availability as more people transit their routine work online, as well as research funding and investment, accelerating the development of artificial intelligence (AI). Recent successes in AI techniques across various fields such as object detection [9; 10] and machine translation [11; 12] have proved their effectiveness in extracting information from a massive amount of data. Hence, AI applications in CPDSs are gaining an explosive amount of research interest as the promising solution to the above-mentioned challenges. The complexity of coupling the physical and cyber layers in distribution systems often requires developing and even combining various AI techniques to provide dedicated and sophisticated solutions to certain domains; Figure 1 summarizes these application domains and algorithm layers. Yet, currently, there is a lack of timely review of AI techniques developed during this period specifically for the CPDSs. In order to fill this gap, this paper provides a comprehensive and detailed review of various state-of-the-art AI-based algorithms developed in the CPDS. Furthermore, current challenges and potential research directions are discussed, while interoperability with smart buildings and intelligent transportation systems, as the latest trend, is highlighted. Figure 1: CPDSs consist of cyber and physical layers and interconnect architectures in a physical distribution system environment by utilizing communication, control, and computing resources through two-way information and data stream, as well as algorithms. The rest of this paper is organized as follows.