This paper explores the use of stochastic optimization techniques to address the aircraft allocation problem under uncertain passenger demand. The proposed stochastic allocation model successfully meets the study’s objectives by demonstrating how uncertainty in passenger demand can be effectively incorporated into aircraft assignment decisions through a two-stage stochastic programming framework. Simulation results across multiple demand scenarios show that the model provides stable and adaptive allocations that minimize total cost while maintaining service quality, even under high variability. Incorporating the simple recourse approach enables post-decision flexibility, reducing penalties for unmet demand, and the use of Geometric Brownian Motion (GBM) offers a realistic representation of continuous demand fluctuations over time. These outcomes confirm the model’s practical value in bridging deterministic planning and real-time decision environments. While future research will focus on extending the model to a Markov Decision Process (MDP) framework and integrating real-time data streams, the current results establish a solid foundation by quantifying how uncertainty directly impacts fleet utilization, cost efficiency, and service reliability.