Unmanned aerial systems/vehicles (UAS/UAVs) are widely employed for inspecting highvoltage (HV) Tx lines, characterized by elevated electric (E) and magnetic (H) fields. Operating on batteries, these UAVs are equipped with various electrical sensors, microprocessors, and motors, all susceptible to E/H field effects. This paper explores the distribution of E/H fields in multiple HVTx lines and a microwave tower. Data was collected from one 250kV DC , four AC Tx lines (69kV, 230kV, 345kV, and 500kV), and one microwave tower, utilizing DJI UAVs (M2EA, M30, and M300) equipped with onboard setups. Measurements included E field in V/m, H field in mG, Battery voltage in V, Battery current in A, Battery percentage, Battery Temperature in F, latitude, and longitude. Preliminary findings highlight larger E/H field levels within AC Tx lines than DC Tx lines. The paper discusses conditions influencing E/H field strength during UAV operation. Additionally, a proposed multi-staged random forest regressor (RFR) and k-nearest neighbor (KNN) hybrid machine learning (ML) model forecasts UAV battery drain. Results indicate that the hybrid RFR and KNN model yields lower MAPE values compared to standalone models. INDEX TERMS Battery, electric field, hybrid model, KNN regressor, machine learning, magnetic field, power drain, random forest regressor, transmission lines, unmanned aerial vehicle.