Electricity theft presents a significant challenge for power utilities globally, leading to billions of dollars in annual financial losses and threatening the reliability of power systems. The growing popularity of smart grids increases the necessity for effective fraud detection mechanisms. This article provides a comprehensive analysis of several types of fraud impacting smart grids, including physical diversion, data manipulation, and cyber-attacks targeting Advanced Metering Infrastructure (AMI). The research investigates sophisticated approaches for fraud detection, including machine learning methods, hybrid CNN-LSTM architectures, and real-time anomaly detection systems incorporated into smart meters. The role of deep learning models, namely Convolutional Neural Networks (CNN) combined with Long Short-Term Memory (LSTM) networks, is highlighted in their efficacy in detecting fraudulent activities and managing data imbalance. Current implementation case studies are presented, highlighting actual deployment challenges, such as data privacy concerns and vulnerability to advanced attacks. This study summarizes significant findings, recommends improvements for augmenting fraud detection capabilities, and identifies prospective research domains, emphasizing the imperative of collaboration among stakeholders to develop secure and efficient smart grid systems.