With the increasing complexity and scope of software systems, improving their performance has become one of the most important challenges in software engineering. Machine learning, as an advanced branch of artificial intelligence, has provided the opportunity to provide innovative and intelligent solutions to this challenge. In this paper, we have comprehensively and systematically reviewed the applications of machine learning algorithms in improving the performance of software systems. The main themes include increasing efficiency, improving reliability, developing scalability, improving security, and facilitating software maintenance processes. The results of this review show that algorithms such as deep learning and support vector machines have been able to create remarkable developments in various fields. In addition to analyzing current achievements, this paper identifies research gaps and provides directions for future research. The findings of this research are an important step towards integrating machine learning with software engineering to build smarter and more effective systems.