The problem of lane detecting is challenging to resolve. It has piqued the curiosity of the computer vision field for many years. In essence, lane detection is a multi-featured detection problem. It demonstrates a real-time challenging problem for computer vision and machine learning systems. Several machine learning methods are utilized for lane detection, but they are often used for classification as opposed to feature creation. On the other hand, contemporary machine learning techniques have been demonstrated to be effective in feature detection tests and can be used to locate features with a high recognition value. Unfortunately, in terms of lane detecting accuracy, these methodologies have not yet been properly applied. We employ Hough Transform- and convolutional neural network-based computer vision methods to develop the tracking road lanes in this paper. These methods are performing well in real time testing and produce some novel results. It will help to enhance lane detection accuracy to alleviate road traffic security.