This paper studies contextual factor driven mobility prediction to optimize energy efficiency in drone deployment for cellular network support. It addresses the challenge of aligning aerial base station movement according to predicted user demand hotspots while considering drone air dynamics. The proposed method utilizes real-time Contextual data streams such as user location traces, activity patterns and weather conditions. This information is learned by a lightweight on-board neural network to predict short-term trajectory patterns. The study compares mobility prediction incorporating real-time context verses alternatives relying solely on historical data. It concludes that fusing dynamic situational intelligence with learned environmental representations offers superior accuracy in forecasting drone movement needs. This guidance is then used for energy-efficient path planning, hovering and cell association optimization through feedback control of drone positioning. The predictive approach presents more efficient dynamic response and resource utilization for heterogeneous application scenarios versus benchmark methods.