Time-efficient route planning is a significant research area of Internet of Vehicular Things. Optimal route selection is important to reach the destination in minimal time. Further, energy efficiency is vital for route planning in a sustainable environment. To address these issues, this paper proposes a federated learning and genetic algorithm-based green edge computing framework for optimal route planning in Internet of Vehicular Things. The vehicles are connected to the road side unit. The road side unit processes the image and video of the road, and predicts the number of vehicles on the road. For video processing Region-based Convolutional Neural Network is used. The road side units send the result and the local model parameters to the regional server. The regional server determines the optimal route using modified genetic algorithm, and sends it to the vehicles and the cloud. Also, the regional server updates its model and sends the updated model parameters to the road side units. The road side units update their local models accordingly. The regional server also sends the model parameters to the cloud, and the cloud updates the global model. The cloud sends the updated model parameters to the regional servers. The regional servers update their models accordingly. The results present that above 90% accuracy is achieved by the proposed model. The results also present that using modified GA the proposed approach reduces time and power consumption to find the optimal route by ∼62% and ∼66% than the cloud-only model.