This study involves developing an unsupervised machine-learning (ML) framework (an artificial intelligence (AI) approach) for predicting micro-scale wind and turbulence. The localized wind and turbulence information from AI is a potential enabler for path planning of urban-scale drone operations and for use in smart-city through building-integrated renewable energy systems. In urban-city, the wind and turbulence is influenced by buildings and terrains and these complex local physics needs to be captured by the AI model. The trained ML in this work uses two input parameters: the meso-scale wind speed and the meso-scale wind direction in-order to predict the micro-scale building-induced wind and turbulence for regions around the Prague city centre. The performance of ML model has been compared with a traditional computationally-intensive computational Fluid Dynamics (CFD) solutions in terms of accuracy and computational speed-ups. After training, the ML performance has been compared for “unseen test datasets” in both the interpolation range and the extrapolation range of the input meso-scale parameters used in the training dataset. The results indicate that the machine learning model yields reasonably accurate solution for the velocity and turbulence fields for the test cases when the unseen input meso-scale parameters are “within the interpolation range” of training database and the ML infers the flow field in a matter of 5–10 s, i.e. a computational speed-up of over 1000 times as compared to the traditional CFD models. While for extrapolation (out-of-distribution) parameter test case, as expected, the ML model has scope of improvement in accuracy in flow-field. Overall, the ML-predictions due to its computationally efficiency can enable quick path planning and decision-making during drone operations, which is not possible with traditional computational fluid dynamics solution, thus highlighting the potential of machine learning for planning drone operations.