The paper presents a holistic Intrusion Detection System (IDS) that combines Machine Learning (ML) and Deep Learning (DL) to detect cyberthreats. The study aims at raising the accuracy of detection without false positives by combining the Host-Based IDS (HIDS) and Network-Based IDS (NIDS) frameworks. In addition, the study has experimented the various threat detection algorithms in dependence on logistic regression, k-nearest neighbors, naive Bayes, decision tree, random forest, and XGBoost, which can be applicable on the anomaly detection on top of the NSL-KDD dataset containing critical network activity attributes. It chose every model that was able to predict known threats by utilizing ML or DL for classifying complex patterns. Analysis suggests that the optimized random forest with Principal Component Analysis (PCA) will achieve the balance between accuracy and efficiency in computing. The observations made using XGBoost highlighted slight overfitting, which would require more fine-tuning. The artificial neural network balances depth and regularization to capture non-linear patterns without overfitting, using dropout and a deep architecture for optimal predictive performance in large feature spaces or intricate patterns. This study innovatively combines ML and a novel DL within the context of IDS, thus showing the way in which an integration of anomaly-based and signature-based detection will provide a robust and adaptive solution toward known and unknown threats within dynamic cyberenvironments.