Memory leak is a common code bug for C programs which uses explicit memory management mechanisms. At present, the main detection methods of memory leaks are static analysis and dynamic detection. Dynamic detection has huge overhead and it is highly dependent on test cases. Static analysis is widely used by academic and industry, but there are a large number of false positives, which need to be manually confirmed. Inaccuracy in the analysis of pointers, branch statements, and global variables leads to false positives in static analysis of memory leaks. In this study, an intelligent detection method is proposed for memory leak. By using machine learning algorithms to learn the correlation between program's features and memory leaks, a machine learning classifier is built and applied to improve the accuracy of static analysis of memory leaks. Firstly, a machine learning classifier is trained. Then, the sparse value flow graph (SVFG) starting from allocation should be constructed by using the static analysis, the features related to memory leaks can be extracted from the SVFG. Lastly, the memory leaks are detected by using rules and machine learning classifier. Experimental results show that the proposed method is effective in analyzing pointers, branch statements, and global variables, and can reduce the false positives of memory leak detection. At the end of this paper, the feasibility of future research and the upcoming challenges are presented.