In today’s digital world, where cyber threats grow smarter by the day, Intrusion Detection Systems (IDS) have become a cornerstone of cybersecurity. Yet, they still struggle with real world challenges too many false alarms, difficulty scaling in cloud and Software-Defined Networking (SDN) environments, and an ongoing battle to catch elusive zero-day attacks. This study introduces a smarter, more adaptable IDS framework that combines the strengths of deep learning and ensemble models. By using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deep feature extraction, and pairing them with powerful classifiers like Random Forest, Support Vector Machine (SVM), and Gradient Boosting, we create a layered defense system. To keep the system fast and transparent, we integrate feature optimization through Principal Component Analysis (PCA) and bring in Explainable AI tools such as SHAP and LIME, ensuring not only performance but also interpretability. Tested on the UNSW-NB15 dataset, our framework significantly boosts detection accuracy, lowers false positives, and shows strong promise for real-time use in cloud and SDN settings. Ultimately, this research aims to build a truly intelligent IDS scalable, efficient, and ready to face the ever-evolving landscape of cyber threats.