Workloads in modern AI networks generate highly heterogeneous traffic, with conflicting demands for low latency and high throughput. Existing load balancing strategies often overlook the differing transmission requirements of heterogeneous traffic. To address this gap, this paper proposes a heterogeneous traffic-aware load balancing (HaLB). Specifically, HaLB integrates three key influencing factors by calculating a heuristic function to compute the transition probability for selecting the next hop, ultimately choosing the optimal routing path. NS-3 simulation experiments demonstrate that HaLB significantly outperforms existing advanced schemes. It not only achieves a 24% reduction in average flow completion time (AFCT) but also substantially lowers the deadline miss rate by 55%.