Unmanned aerial vehicles (UAVs) or drones are rapidly gaining popularity in the field of remote sensing for capturing images with ultra-high spatial resolution while flying at lower altitudes. Development of highlyefficient miniaturized sensors and the use of geospatial image processing techniques have been immensely helpful in growing this technology as the most sought-after and sophisticated remote sensing technique at a relatively lower cost. Parking lot occupancy detection, one of the current drone based application area, can be used for effective management of parking spaces, to reduce queues, minimize the time required to find an area, and to issue tickets in cases of parking violations. Visual information based parking lot monitoring techniques are mostly tailored for specific applications, and they lack generalization. In this paper, a UAV-assisted quick and efficient monitoring solution is proposed for real-time parking occupancy and car number plate detection. In this approach, a drone-mounted camera has been used to capture images of the parking lot under consideration. Initially, the parking lot is being mapped using drone-coordinates, and then a dynamic programming algorithm is used to determine the shortest route to cover multiple parking lots in minimum time to capture maximum number pictures solved by the Traveling Salesman Problem (TSP). For capturing images, an optimum location is used, such that the drone can cover the maximum area of a parking space in minimum time. Depending on the parking area under investigation, paths altitudes and gimbal angles of the drone is changed dynamically while capturing images. Then a deep neural network based parking lot occupancy monitoring system is used to determine the number of occupied and vacant spots in a parking lot. The drone captured images of each parking spot are then tested with a pre-trained model based on car and non-car images. Then the automatic license plate recognition (ALPR) algorithm is used for parking rule enforcement. Finally, experimental results are verified using a web based application that is connected with a cloud database.