The transition from traditional to smart grids has been driven by the pursuit of greater efficiency, reliability, and consumer engagement. While smart grids offer numerous benefits, they are vulnerable to cybersecurity threats. Intrusion detection systems (IDS) are indispensable tools for safeguarding smart grid operations by identifying and preventing malicious attacks. This research investigates the application of various IDS models, classifiers, datasets, and algorithms in smart grid environments. The study underscores the importance of using datasets specifically designed for smart grid networks to ensure accurate and reliable IDS performance. Moreover, the research demonstrates the potential of distributed approaches and advanced algorithms in enhancing IDS capabilities, thereby bolstering the security and resilience of smart grid infrastructure.