Seamless mobility management is an ability to provide the various services during the communication in wireless heterogeneous networks. Due to the random mobility of the mobile terminals, the connectivity between different mobile devices gets lost. In order to provide the lossless connectivity between the mobile devices, the handover from the point of current attachment to another point is necessary. To improve the Seamless mobility management and traffic control, an efficient model called Generalized Light Gradient Boost Decision Tree-based Traffic-Aware Seamless Mobility (GLGBDT-TASM) model is introduced in the heterogeneous network. When a mobile node in the network moves out of its communication range, the signal strength of the nodes is calculated. Based on the signal strength estimation, the Generalized Light Gradient Boost Decision Tree classifier categorizes the mobile nodes into the weak and strong signal strength with the threshold value. The boosting algorithm initially constructs’ weak learners i.e. binary decision tree to identify the weak signal strength of the mobile node. Then the ensemble classifier combines the results of weak learners and minimizes the generalization error. This helps to perform the handover only with the weak signal strength of the node resulting in minimizes the redundant handover. In addition, the weak signal strength of the mobile node from the current attachment point handover towards the nearest available attachment point to improve the continuous data delivery. Followed by, bandwidth availability is measured for reducing the packet loss due to thenetwork traffic resulting in improves the seamless data delivery between the nodes. The simulation is carried out to evaluate the performance of the GLGBDT-TASM model with two related approaches. The results show that the GLGBDT-TASM model effectively improved traffic-aware seamless mobility in a heterogeneous network with minimum delay and packet loss as well as a higher data delivery rate as compared to state-of-the-art methods.