Traffic prediction is essential for modern transportation systems, enhancing traffic management and urban planning. Accurate predictions of traffic flow and speed are crucial for understanding road usage, mitigating congestion, and providing real-time traffic monitoring and dynamic route guidance, thus improving road safety and infrastructure efficiency. Traditional research has often focused on predicting traffic flow or speed independently, leading to higher resource consumption due to the need for separate models. Few studies have explored the simultaneous prediction of both metrics, with recent attempts failing to account for spatial correlations, resulting in suboptimal performance. To address these challenges, we propose MTNet, a multi-task learning framework for joint traffic flow and speed prediction. MTNet employs a Transformer-like Encoder-Decoder architecture to process and enhance feature representations, capturing complex spatio-temporal correlations. Specifically, MTNet extracts intra-task dependencies using a cross-task interaction module and models task-specific spatiotemporal dependencies using spatial and temporal-aware modules with cascaded residual structures. Additionally, spatio-temporal positional encoding is integrated to increase awareness of long-term and long-distance dependencies. Extensive experiments on three diverse traffic datasets-Manchester, PeMSD4, and PeMSD8-demonstrate that MTNet significantly outperforms state-of-the-art methods in both traffic flow and speed prediction. MTNet achieves substantial improvements in prediction accuracy and efficiency, striking an optimal balance between performance and computational resource usage.