Abstract: Traditional medical equipment failure warning heavily relies on manual inspection, resulting in delayed warning and low inspection accuracy. The study proposes using long short-term memory networks to construct a medical equipment fault warning model, and incorporating genetic algorithms on this basis to optimize the overall performance of the model and improve warning efficiency. The experiment collected the fault records of blood gas analyzers from Lin'an People's Hospital in the past three years as experimental materials. Through comparative verification, the experimental results show that the proposed model exhibits good convergence speed and accuracy on both Ackley and Rastrigin test functions. In addition, the model can predict medical equipment failures well, with a recall rate of up to 93.2%. Furthermore, the accuracy of the model is above 90% in different types of fault warnings. In summary, the proposed model effectively improves the accuracy of medical equipment fault warning and has practical application value, which can be widely used in medical equipment fault warning systems.