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In recent years, it has been observed that it becomes time-consuming and cumbersome job to find a vacant parking lot, especially in urban areas. Thus, it makes difficult for potential visitors or customers to search a vacant space for parking their vehicles and keeps on revolving round the parking area which not only increases frustration level but also wastes time and energy. In order to get an optimal parking lot immediately, there is a requirement of an efficient car-park routing systems. Current systems detecting vacant parking lots are either based on very expensive sensor based technology; or based on video based technologies which do not consider various weather conditions like sunny, cloudy and rainy weather. In the proposed work, a hybrid model is designed for detecting outdoor parking which detects the empty spaces available in the parking lots and the spaces/ slots getting vacant in the real-time scenario. This model is based on training, validating and testing the images (dataset) collected from various heights and angles of different parking areas stored in the repository. In this research, more advanced feature extractors and machine learning algorithms are evaluated in order to find the vacant parking lots in the outdoor park-ing areas.