This research work aims to develop a multi-commodity multi-objective relief distribution model for emergency logistics, which optimizes vehicle routes and resource allocation to disaster-affected areas. It involves formulating a mixed integer linear programming model that addresses the vehicle routing and resource allocation issues, priority for relief distribution, capacity, subtour elimination, and d-relaxed priority constraints. The developed solution aims to minimize routing costs and penalty costs due to unsatisfied demand, serves nodes in the order of their priority levels, ensures no oversupply to demand nodes, maintains inventory levels, and minimizes penalty costs for unsatisfied demand across all nodes. The models are solved in two stages: stage-1 focuses on vehicle routing, and stage-2 on resource allocation using the CPLEX library in Python. The model obtained is applied to real case data of Alappuzha-2019 floods from which, the optimal routes and resources to be allocated are obtained.