Geolocation services, subsequently, have made it possible to seek routes on any text-based platform among road networks. The study discusses Clues-Based Route Search (CBRS), which allows users to keyword or spatially relate inputs so that it further helps in route prediction. The greedy algorithm’s implementation has been used in combination with optimization via branch-and-bound to hasten results while omitting unnecessary search points. The implementation of AB-tree and PB-tree contributes to overall efficiency in the storage of keyword and spatial data through the two-hop label index as an optimized person identifier while reducing index size. Addressing network optimization challenges using a newly formulated framework that houses advanced algorithms for load balancing, latency minimization, and throughput maximization is the contribution of the study. In this article, a hybrid optimization model, combining machine learning techniques with conventional network design approaches, is presented to dynamically adapt to network conditions and user demands. The improvements in terms of latency reduction and bandwidth utilization brought about by the proposed techniques were demonstrated through simulations run on large-scale network datasets and showed significant enhancements over previous methodologies. The evident adaptability and scalability of the framework indeed promote its versatility in applications covering cloud computing, the Internet of Things, and telecommunication. Future work aims to extend this model to multi-layered networks and investigate its potential in heterogeneous network environments.