Traffic forecasting applies machine learning and deep learning techniques to accurately predict future traffic conditions, helping cities reduce congestion and improve daily travel for everyone. Traffic forecasting is about using smart technology like machine learning and deep learning to predict traffic patterns before they happen. By analyzing data from sensors, GPS, and historical trends, these methods can forecast where traffic will be heavy and when, making it easier to plan for smoother, faster commutes. Also, these predictions help cities manage traffic more efficiently, cut down on delays, and improve road safety, while also enabling real-time decision-making to prevent congestion. As cities get smarter and technology advances, traffic forecasting continues to improve, offering more accurate estimations and valuable insights into the road networks’ mechanics and spatiotemporal relations that benefit everyone on the road.