This paper discusses the design and implementation of a modern traffic flow prediction system using data from street surveillance cameras deployed at the website 0511.vn. The core objective of the research was to develop an efficient prediction model based on direct image analysis and real-time data, providing instant traffic information and forecasting short-term traffic trends. Initially, it is necessary to identify and evaluate existing image processing and machine learning methods to filter out and classify vehicles from the collected video data. Subsequently, the author designed models combining ARIMA and LSTM methods to predict the density and movement of vehicles on the roads. These methods were tested and optimized through a series of experiments on historical data and real-time data collected from 0511.vn, marking a significant advancement in applying video surveillance technology to urban traffic management. The research results not only contribute to the field of data science and image processing but also have practical potential in supporting the decision-making of traffic management agencies and improving the community’s commuting experience.