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The rising number of technological advanced devicesmaking network coverage planning very challenging tasks for network operators. The transmission quality between the transmitterand the end users has to be optimum for the best performance outof any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout thewhole operational stage. Any coverage hole in network operators’coverage region will hamper the communication applications anddegrade the reputation of the operator’s services. Presently, thereare techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radioenvironment and high time consumption do not allow to meet therequirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deployUAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop(HiTL) model. Later, we formulate an optimisation problem for3D UAV-BS placement at various angular positions to maximisethe number of users associated with the UAV-BS. In summary, wehave illustrated a cost-effective as well as time saving approach ofdetecting coverage hole and providing on-demand coverage in thisarticle.