Unmanned Aerial Vehicles (UAVs) are increasingly employed across various domains, including communication, military, and delivery operations. Their reliance on the Global Positioning System (GPS) renders them vulnerable to GPS spoofing attacks, in which adversaries transmit false signals to manipulate UAVs’ navigation, potentially leading to severe security risks. This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for GPS spoofing detection. Although GA–neural network combinations have existed for decades, our method expands the GA’s search space to optimize a wider range of hyperparameters, thereby improving adaptability in dynamic operational scenarios. The framework is evaluated using a real-world GPS spoofing dataset that includes authentic and malicious signals under multiple attack conditions. While we discuss strategies for mitigating CPU resource demands and computational overhead, we acknowledge that direct measurements of energy consumption or inference latency are not included in the present work. Experimental results show that the proposed LSTM–GA approach achieved a notable increase in classification accuracy (from 88.42% to 93.12%) and the F1 score (from 87.63% to 93.39%). These findings highlight the system’s potential to strengthen UAV security against GPS spoofing attacks, provided that hardware constraints and other limitations are carefully managed in real deployments.