Turbulence is by far the number one concern of anxious passengers and a cause for airline injuries. Apart from causing discomfort to passengers, it also results in unplanned downtime of aircrafts. Currently the Air Traffic Control (ATC) and the meteorological weather charts aid the pilot in devising flight paths that avoid turbulent regions. Even with such tailored flight paths, pilots report constant encounters with turbulence. The probability of turbulence avoidance can be increased by the use of predictive models on historical and transactional data. This paper proposes the use of predictive analytics on meteorological data over the geographical area where the aircraft is intended to fly. The weather predictions are then relayed to the cloud server which can be accessed by the aircraft planned to fly in the same region. Predictive algorithms that use Time series forecasting models are discussed and their comparative performance is documented. This paper further brings out the benefits of embedding additional sensors in the airframe, use of data storage techniques, data analytics and big-data in turbulence avoidance and extended benefits to airline MRO (Maintenance, Repair and Overhaul) to better manage lifecycle information of aircraft components.