This paper introduces an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The model employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. Synthetic Minority Over-sampling Technique (SMOTE) is used for data balancing, followed by intrusion detection systems and network traffic categorization. The performance of a deep learning-based feed-forward neural network (DFNN) algorithm is evaluated across three datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. The proposed algorithm demonstrates high accuracy, precision, recall, and F-measure, outperforming benchmark machine learning approaches. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.This paper introduces an intelligent hybrid model that leverages machine learning and artificial intelligence to enhance the security of Wireless Sensor Networks (WSNs) by identifying and preventing cyberattacks. The model employs feature reduction techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), along with the K-means clustering model enhanced information gain (KMC-IG) for feature extraction. Synthetic Minority Over-sampling Technique (SMOTE) is used for data balancing, followed by intrusion detection systems and network traffic categorization. The performance of a deep learning-based feed-forward neural network (DFNN) algorithm is evaluated across three datasets: NSL-KDD, UNSW-NB 15, and CICIDS 2017, considering both full and reduced feature sets. The proposed algorithm demonstrates high accuracy, precision, recall, and F-measure, outperforming benchmark machine learning approaches. The study outlines the system configuration and parameter settings, contributing to the advancement of WSN security.