Code smells, indicative of poor code quality and maintainability, pose significant challenges to software development teams. Despite the availability of automated tools, accurately identifying and resolving these issues remains a complex task. To address these limitations, this research addresses the shortcomings of existing code smell identification and resolution methods. By leveraging cloud-based crowdsourcing and interactive learning, the authors propose a novel platform that effectively identifies and rectifies inefficient code design choices that evade traditional compiler detection. The approach aims to overcome the limitations of automated tools, which often struggle to achieve high accuracy, by providing a semi-automated refactoring strategy that empowers developers to make informed decisions regarding recommended code modifications. Through a combination of quantitative and qualitative analyses, the research demonstrates how the proposed solution improves the efficiency and effectiveness of code smell identification and resolution by addressing key challenges such as a lack of knowledge, insufficient validation support, and limited collaborative opportunities.