Cascading Style Sheets (CSS) is one of the most essential tools in styling front-end web development. In a web application, HTML code provides the structural foundation whereas CSS provides the visual appearance, like font style, colors, layout, spacing, and other elements. As web applications continue to grow in complexity with time, maintaining high-quality, efficiency, and clean code throughout the document has become increasingly challenging. As a result of this problem a larger quantity of developers are increasingly moving into CSS preprocessors or web frameworks. However, with or without knowledge of CSS, using these tools can unintentionally lead to code smells – patterns or practices found in the CSS code that are not necessarily incorrect but can hinder the maintainability, scalability, performance, and readability of the code. There are several existing tools like CSS Lint, W3C CSS Validator and CSS nose that address the quality and validation of the CSS code. However, these tools don’t incorporate machine learning and do not provide real time feedback. This paper investigates the CSS code smells that take place and explores how these code smells can be reduced by using machine learning. Given that Visual Studio (VS) code is one of the major platforms for developers to use CSS code, this research focuses on incorporate this concept as a plugin to this IDE. The proposed plugin will aim to provide real-time feedback, actionable insights, and enabling developers to write more cleaner, optimized and more maintainable CSS.