This review examines the application of artificial intelligence (AI) in detecting anomalies in smart grids (SGs). The study analyzes research from 2011 to 2023, identifying seven key areas of anomaly detection: data integrity attacks, unusual consumption behaviors, network intrusions, network infrastructure anomalies, electrical data anomalies, cyber-attack detection, and anomaly detection devices. The paper highlights the use of various AI techniques, including machine learning, regression, decision trees, deep learning, support vector machines, and neural networks. It also discusses emerging methods such as federated learning, hyperdimensional computing, and graph-based approaches. The review emphasizes the growing trend towards hybrid solutions and anomaly detection frameworks. Challenges include the need for methods that do not rely heavily on data or network model knowledge. The study concludes that AI is driving the evolution towards next-generation smart grids, with a focus on real-time, accurate, and efficient anomaly detection. The paper also notes the importance of addressing cybersecurity threats, such as false data injection, network intrusions, and cyber-attacks, which are increasingly prevalent in SGs. The review provides a comprehensive overview of current research and identifies areas for future investigation, including the integration of digital twins and further refinement of detection methods.This review examines the application of artificial intelligence (AI) in detecting anomalies in smart grids (SGs). The study analyzes research from 2011 to 2023, identifying seven key areas of anomaly detection: data integrity attacks, unusual consumption behaviors, network intrusions, network infrastructure anomalies, electrical data anomalies, cyber-attack detection, and anomaly detection devices. The paper highlights the use of various AI techniques, including machine learning, regression, decision trees, deep learning, support vector machines, and neural networks. It also discusses emerging methods such as federated learning, hyperdimensional computing, and graph-based approaches. The review emphasizes the growing trend towards hybrid solutions and anomaly detection frameworks. Challenges include the need for methods that do not rely heavily on data or network model knowledge. The study concludes that AI is driving the evolution towards next-generation smart grids, with a focus on real-time, accurate, and efficient anomaly detection. The paper also notes the importance of addressing cybersecurity threats, such as false data injection, network intrusions, and cyber-attacks, which are increasingly prevalent in SGs. The review provides a comprehensive overview of current research and identifies areas for future investigation, including the integration of digital twins and further refinement of detection methods.