The success of a software system mostly depends on how effectively and carefully the requirements are understood, prioritized, and handled. Defects, cost escalation and project failure often occur because of misconception of requirements. While current requirement engineering methods have tried to solve these problems, still there are many that struggle to classify perfectly and prioritize needs. This is especially applied for non-functional requirements, where systematic validation remains as a notable gap in the process. This study suggests a structured FR/NFR Defect Decision Ontology Framework that combines ontology-based thinking including machine learning techniques. The framework is performed on a dataset consisting of 6,086 requirements (3,964 FRs and 2,122 NFRs). Defects are automatically identified by using ontology-driven rules, which leads to 458 defective occurrences. Evaluation has been done with a layered 80/20 train–test split with 10-fold cross-validation. FR/NFR classification, defect detection, defect type classification, and severity classification are four classification tasks which are performed by using models such as Naive Bayes, Support Vector Machine (SVM), Logistic Regression, and Random Forest. The result shows strong performance with the highest accuracy, 87.68% for FR/NFR classification, 97.29% for defect detection, 88.76% for defect type classification, and 82.02% for severity classification. The findings indicate that NFR defects are more complex and less traceable than FR defects. The framework will help to improve both accuracy and understandability that supports more effective requirement analysis and decision-making in software engineering.