The security of hardware intellectual properties (IPs) has become a significant concern, as the opportunity for piracy, reverse engineering, and malicious modification is increasing. Hardware obfuscation has been studied as a potent method to protect against all these attack vectors. However, most of the existing obfuscation techniques have been successfully compromised, where many inherent functional or structural vulnerabilities in these techniques are utilized to reveal the obfuscation key or retrieve the original design. In this article, we introduce LeGO, a learning-guided obfuscation framework that overcomes known vulnerabilities in a scalable and systematic manner, leading to a robust and lightweight locking mechanism. The proposed framework is guided by our security evaluation process that performs a thorough assessment of an obfuscated IP against various attacks and identifies the vulnerabilities. It then judiciously selects and applies a set of design modification steps or rules that can eliminate these vulnerabilities. Such a rule-based obfuscation process has the distinctive capability to address all existing as well as emerging attacks through the learning of appropriate design transformation steps that prevent these attacks. We present an efficient strategy to apply these rules on a design, while resolving any conflict. Our evaluation of the LeGO framework on a set of ISCAS85 and open-source IP benchmarks has shown promising results in terms of robustness against diverse attacks with an average of area, power, and delay overhead of 39%, 45%, and 15%, respectively.