Identification of parking slots using machine learning models is not only required for autonomous vehicles but also helpful to human beings. It assists the drivers in parking their vehicles, which helps to reduce the chances of accidents, increase the utilization of parking space, and reduce time to park the vehicle and many more benefits. Several computer vision algorithms are used to detect parking spaces. These algorithms require a lot of image data to detect objects and distance, independent of environmental factors such as light. In this paper, the author evaluated the performance of 3D Convolutional Neural Network (3D CNN) model for the parking slot detection from dashcams. The 3D CNN is also combined with the Long Short-Term Memory (LSTM) model to evaluate performance. 3D CNN achieved an accuracy of 90.93%, whereas the combination of 3D CC and LSTM achieved 91.1% accuracy.