In real life, there are a lot of obstacles that are present around you at all times. It is easier for someone who has the capability to see things and react accordingly but in real life scenarios, there are a large number of people that are visually impaired. This project takes the training approach to effectively detect objects that are present in the environment and warn them about the obstacles that lie there. In this project, a system has been built by effectively training hundreds of thousands of data to efficiently detect objects in real life and the intermediate result from the detection has been converted to speech form. To train the dataset, Tenserflow GPU has been used and to accurately detect the objects, You Only Look Once algorithm has been used. After that, for conversion of the result to speech, Google Text to Speech has been used. The use of machine learning has been done to effectively train models. The use of Coco Dataset model has also been done to detect 80 predefined classes. It comprises of over 1,23,000 data sets for the accurate and reliable detection of the objects. The tests on the detection show the accuracy of over 70% on the detected objects but due to the lack of computational device available, the time accuracy and frames per second have been considerably slower. Both have only an accuracy of around 20%. The application can be successfully implicated for people that are visually impaired. This algorithm has been specifically chosen as it is a state-of-the-art algorithm. Initially, ScaleInvariant Feature Transform algorithm was used which turned out to be slow and less efficient than the algorithm we currently used in the system.