Intrusion detection is essential for safeguarding computer networks against malicious activities. This work integrates three advanced approaches to achieve robust intrusion detection, leveraging two distinct datasets. Firstly, a Graph Neural Network (GNN) and Tabular Transformer model utilize the KDD Cup 99 dataset to classify network intrusions, achieving best-in-class accuracy by effectively modeling complex relationships within the data. Secondly, a Generative Adversarial Network (GAN)-augmented Multilayer Perceptron (MLP) employs the NSL-KDD dataset to enhance data diversity, generating realistic synthetic samples that improve classification performance. Lastly, a hybrid framework combining Variational Autoencoders (VAEs) and GANs, also leveraging the NSL-KDD dataset, addresses class imbalance and data synthesis challenges, producing high-quality synthetic data while retaining essential features. Each approach achieves its best accuracy on its respective dataset, demonstrating significant advancements in intrusion detection accuracy, reducing false alarm rates, and ensuring computational efficiency.