<p>Maximizing the efficiency of the transport and reducing the time of delivery are supported by real-time traffic forecasting during the era of smart logistics. AI and Big Data make intelligent traffic forecasting possible, which allows making a better decision aiding logistics company. The majority of the existing approaches lack the means to address the dynamic traffic conditions effectively, as most of them are likely to absorb both the temporal correlations and the spatial dependencies at an unsatisfactory level. This translates to wrong predictions and inefficient route planning. To address these issues, we propose a Hybrid Deep Learning-Based Traffic Prediction on cloud platform Model (HDL-TPM), which is a version of Long Short-Term Memory (LSTM) networks and Graph Neural Networks (GNNs). STM takes into account changes in traffic flows in sequence, whereas GNN captures spatial networks of roads. Even while cloud-based learning models are increasingly ubiquitous, this research is unique as it uses a unified cloud-native spatiotemporal pipeline that combines distributed data collection, GNN-based spatial modeling, and LSTM-driven temporal forecasting. It allows for scalable, parallel, real-time traffic prediction from a range of urban sensor sources, growing beyond traditional batch-driven cloud systems. The hybrid strategy provides scalable traffic forecasts through big data processing in real-time. The model handles large traffic datasets, predicting congestion precisely and optimizing logistics routes. Through the learning process of past and current traffic data, HDL-TPM improves traffic flow forecasting accuracy, preventing delay in intelligent logistics. The experimental results show that HDL-TPM performs better compared to traditional models, providing greater prediction accuracy and better efficiency in intelligent logistics operations.</p>