High-Speed Machining (HSM) has been extensively investigated for its potential to enhance machining efficiency and precision. A persistent challenge in HSM is the formation of micro-burrs, which adversely affect surface quality and production efficiency. Numerous studies have explored the optimization of machining parameters, including cutting speed, feed rate, and tool geometry, to address this issue. Advanced optimization techniques, such as Genetic Algorithms (GAs), Artificial Neural networks (ANNs), and hybrid methods, have shown significant promise in minimizing burr formation while maintaining high machining efficiency. A comprehensive review was conducted to identify critical research gaps and propose effective solutions for minimizing micro-burr formation in HSM. Existing studies were found to lack a unified strategy integrating real-time monitoring, tool design, and lubrication innovations with advanced optimization algorithms. A hybrid optimization method combining GAs with ANNs was identified as the most effective approach. This method integrates the exploratory capabilities of GAs with the predictive power of ANNs, enabling dynamic and adaptive optimization of machining parameters. The hybrid method has been shown to optimize machining processes by modeling parameter interactions and providing real-time adjustments. It was highlighted that incorporating real-time monitoring systems and advanced tool coatings could further enhance this framework. Future directions include the exploration of environmentally sustainable lubrication techniques, integration with IoT-enabled systems, and adaptation to advanced material machining. This research emphasizes the importance of integrating advanced optimization techniques with practical applications to achieve precision in HSM, setting a foundation for future advancements in the field. The findings present a robust pathway to improving surface quality and reducing production costs in high-speed machining operations.