This paper reviews studies on anomaly detection in smart grids using artificial intelligence (AI). The review covers publications from 2011 to 2023, focusing on seven key areas: data integrity attacks, unusual consumption behaviors and measurements, network intrusions, network infrastructure anomalies, electrical data anomalies, cyber-attack detection, and devices for detecting anomalies. The paper highlights the use of various AI techniques such as machine learning, regression, decision trees, deep learning, support vector machines, and neural networks. It also discusses novel approaches like federated learning, hyperdimensional computing, and graph-based methods. The review emphasizes the importance of developing robust and intelligent methods to detect anomalies in smart grids, which are increasingly interconnected and automated. The paper concludes by discussing the challenges and future directions in anomaly detection, including the need for more efficient and real-time detection methods that do not rely heavily on network models or large datasets.This paper reviews studies on anomaly detection in smart grids using artificial intelligence (AI). The review covers publications from 2011 to 2023, focusing on seven key areas: data integrity attacks, unusual consumption behaviors and measurements, network intrusions, network infrastructure anomalies, electrical data anomalies, cyber-attack detection, and devices for detecting anomalies. The paper highlights the use of various AI techniques such as machine learning, regression, decision trees, deep learning, support vector machines, and neural networks. It also discusses novel approaches like federated learning, hyperdimensional computing, and graph-based methods. The review emphasizes the importance of developing robust and intelligent methods to detect anomalies in smart grids, which are increasingly interconnected and automated. The paper concludes by discussing the challenges and future directions in anomaly detection, including the need for more efficient and real-time detection methods that do not rely heavily on network models or large datasets.