Class Diagrams are a fundamental element in the software development process, providing an organized visual representation of software components and their relationships. Therefore, ensuring the quality of these diagrams is importance to maintain consistency, design integrity, and project success. Design quality within class diagrams encompasses both data organization methods and systematic assessment of individual system elements. The quality standards of class diagrams determine software system execution speed along with future maintenance needs thus requiring strict attention to minimize later development errors. Higher quality enables designers to discover design issues at early development stages thus allowing corrections prior to advancing to more detailed phases. An innovative tool plays a key role in automating the evaluation process and quality classification which run on the Enterprise Architect platform. This tool merges XML data analysis methods and machine learning intelligent algorithms to supply immediate assessments for software developer high-level designs. Evaluations performed in the early design phase enable software engineers to execute an extensive analysis which reveals both positive and negative features in the first system model. The tool delivers profound design insights to designers which helps them make vital development process improvements throughout early development stages. This helps mitigate risks, enhance the overall quality of the final product, and refine designs before advancing to more complex detailed design stages, ensuring higher performance and greater efficiency in the long term. This tool leverages insights from software quality datasets and applies machine learning techniques to ensure accurate and efficient evaluations. Experimental results demonstrate the tool's efficacy, with the Ensemble (Soft Voting) method achieving the highest accuracy of 96%. Other models performed closely, with the nearest result being 1% lower and others ranging from 2% to 7% below the top accuracy. These outcomes highlight the advantage of combining models to enhance assessment performance.