Phishing attacks continue to be a constant and evolving menace in cyberspace, taking advantage of users' confidence to gain access to private data. To improve the detection of phishing attacks, this research study proposes a novel hybrid architecture that combines the robust spatial feature extraction capabilities of VGG19 with the temporal sequence modelling advantage of a Temporal Convolutional Network (TCN), enhanced with a Multi-Channel Temporal Attention Mechanism. The TCN temporarily captures the deep spatial information that the VGG19 network extracts from embedded URLs and email content to detect time-based attack patterns and sequential dependencies. The model can focus on the most discriminative temporal features, especially in imbalanced datasets, based on the Multi-Channel Temporal Attention Module, which dynamically weights temporal features across different data streams. The proposed Hybrid VGG19–TCN model with Multi-Channel Temporal Attention outperforms conventional CNN-RNN, CNN-LSTM, CNN-GRU, CNN, LSTM, GRU, TCN, and BiLSTM models and other baseline machine learning classifiers regarding accuracy, recall, and AUC, according to an experimental evaluation on three benchmark phishing datasets. The experiment results demonstrate that the proposed model is a more robust and precise solution for detecting advanced phishing attacks than state-of-the-art models, and it can be deployed in real-time phishing detection systems.