Task scheduling means allocating resources to the tasks in such a way that processing can be accomplished in the most optimal way possible. Here the optimal strategy means processing all the tasks in such a way that it incur the least delay, hence the least response time can be achieved by all the tasks. This becomes a major concern when dealing with the Fog computing environment. Fog have limitations on storage capacity and processing power. So all the real time applications cannot be scheduled at the Fog environment. Also it is required to allocate these resources in the most optimal way possible. So it is best suggested to schedule latency critical applications on the fog and other applications to the cloud. This paper proposes a learning based task placement algorithm (LBTP) which used supervised feed forward neural network to recognize the latency critical applications. This algorithm executes in two phases. In the first phase, the features of the tasks serve as the input to this machine learning based framework for decision making regarding whether to schedule task at the fog environment or forward it to the cloud for execution. In the second phase if the tasks scheduled at fog, then tasks are rearranged in the fog queue based on the priority to achieve the most optimal resource utilization. The simulation results were evaluated using the Matlab 8.0 and Aneka 5.0 platform. The results revealed that the proposed method LBTP recorded the best response time, waiting time and resource utilization when compared with the task scheduling at the fog only and task scheduling at the Cloud only environment. LBTP also recorded better results on horizontal scaling by raising the number of virtual machines at the fog environment.