In this paper, we consider multiple models and introduce a new approach named MLAE2 which applies Metareasoning approach for Latency-Aware Energy-Efficient autonomous drones. Metareasoning monitors parameters such as latency and energy consumption for different algorithms and chooses the best one due to the environmental situation changes. To Evaluate our approach we extract the power consumption and latency for both cloud-based computing and edge computing while deploying multiple models on a tiny drone named Crazyflie. The experimental results show that MLAE2 successfully meets the latency constraint while maximizing model accuracy and improving energy efficiency.