Software-Defined Networking (SDN) has transformed contemporary network topology by separating the control plane from the data plane, allowing the network to be centrally and dynamically managed. Its central design, however, also presents enormous security threats that must be mitigated using efficient Intrusion Detection Systems (IDS). This paper proposes an intelligent IDS framework for SDN networks utilizing machine learning algorithms. The proposed method employs the UNSW-NB15 dataset, preprocessing with advanced methods, SMOTE-Tomek resampling, and multi-class classification by XGBoost for attack detection and classification of different attacks. Interactive Streamlit-based dashboards and packet simulation allow real-time observation, filtering of attacks, and visualization of anomalies in detail. Experimental results demonstrate enhanced detection accuracy of 84% using the top 20 features selected that outperform conventional classifiers in precision and responsiveness. The addition of real-time prediction counters, attack distribution graphs, and downloading capability allows for tremendous flexibility when used in live SDN contexts. The project tries to minimize the theoretical/practical implementation gap found among existing IDS models and live deployments with its suggested scalable, interpretable, and effective intrusion detection solution.