Improving connectivity between different cities requires design of highly efficient route planning models that can incorporate, analyze & optimize multiple routing parameters. These models analyze parameters that include city-to-city distance, road quality, toll booths, type of goods, capacity of vehicles, probability of accident on route, and intermediate hop quality. Combination of these parameters is given to an applicationspecific routing model that minimizes route delay, while maximizing driver comfort and reducing probability of route accidents. Existing planning models utilize linear techniques for intercity route planning; thus, they consider only a limited number of parameters due to their reduced computational capabilities. To improve this efficiency, a novel intercity connectivity optimization model for transportation of goods & heavy materials is proposed in this text. The proposed model is able to capture & analyze parameters including route quality, distance between hops, quality of hopping destination, accident probability on route, vehicle capacity, total toll cost, and vehicle density to estimate driving quality, approximate travel time, and risk of accidents. To perform this task, the proposed model utilizes a particle swarm optimization (PSO) model, that assists in performance-based route selection. The model initially generates a random set of solutions for given route, and then optimizes them via cognitive and social learning phases. These optimizations are tested on Intercity Bus Atlas dataset, Intercity Bus Working Group dataset, Intercity bus dataset, & Rio Vista Delta dataset, and parametric evaluation in terms of routing delay, accident probability, driver experience, and cost of routing was evaluated w.r.t. different routes. This performance was compared with various state-of-the-art approaches, and it was observed that the proposed model showcased 9.7% lower routing delay, 6.5% lower accident probability, 15.8% better driver experience, and 8.3% lower routing cost. This performance was observed to be consistent across different vehicle and route types with minimum reconfiguration & modifications, which makes the model highly scalable for a large number of scenarios. Due to these improvements, the proposed model was observed to be applicable for a wide variety of application deployments.