This systematic review examines the essential aspects of cybersecurity in the rapidly evolving field of autonomous electrical systems. As critical components of modern smart grids, these systems are increasingly vulnerable to advanced cyber threats due to their reliance on automation and connectivity. The review synthesizes existing research to identify current vulnerabilities, evaluate the effectiveness of implemented cyber defense mechanisms, and explore emerging trends and technologies aimed at improving the security and resilience of these infrastructures. By systematically analyzing peer-reviewed journals, conference proceedings, and industry reports from the past decade, the review highlights major cyber threats, including ransomware, DDoS attacks, and phishing, which pose significant risks to autonomous electrical systems. It investigates the use of cutting-edge technologies such as machine learning algorithms for detecting anomalies, blockchain for ensuring data integrity, and quantum cryptography for secure communication. A particular focus is given to artificial intelligence's role in predictive cybersecurity, which enables the anticipation of threats before they materialize, enhancing the proactive capabilities of defense systems. The review also examines the application of established frameworks like the NIST Cybersecurity Framework and the Zero Trust Model, which have been instrumental in shaping security strategies for the sector. It discusses both the challenges and opportunities associated with adapting to evolving cyber threats and integrating next-generation technologies into existing systems. This analysis aims to provide cybersecurity professionals, policymakers, and researchers with actionable insights and a comprehensive understanding of the cyber risks and defense strategies related to autonomous electrical systems. Ultimately, the review seeks to contribute to the development of more robust security measures, strengthen grid resilience, and ensure the reliable operation of future energy systems.