One of the key objectives of today's businesses and mills is to predict machine problems. Failures must be avoided, because downtimes represent expensive expenses and a loss of productivity. This is why the number of remaining cycles (RULs) until the failure occurs is vital in machine maintenance. The estimations of the RUL should be based on earlier observations, whenever possible under the same conditions. In the research of RUL estimates, the creation of systems that monitor current equipment conditions is becoming crucial. I employed Long Short Term Memory (LSTM) in my project to determine an aircraft's remaining usable lives. The aircraft's functioning condition is also forecast. The former is done by a regression method, using a classification methodology predicted by working circumstances. In order to estimate operating conditions and remaining usable life of the aircraft, data utilized for LSTM models training are derived from 21 aircraft sensor readings located at different locations with three distinct settings.