Smart contracts are a cornerstone of decentralized applications (DApps), which enable automated transactions on blockchain platforms. However, the immutability and transparency of these contracts make them prime targets for exploitation if vulnerabilities exist. This paper proposes SCANS (Smart Contract Analysis and Notification Systems), a hybrid framework designed to detect security vulnerabilities in smart contracts effectively. SCANS combines static analysis, symbolic execution, and machine learning (ML) to identify known vulnerabilities, such as reentrancy, integer overflows, and gas limit. The tool achieves a detection accuracy of 95% by using Gradient Boosting models for classifying, providing Web3 developers with actionable information for the development of secure contracts.