The expansion of urbanization at a fast rate is making road traffic a rapidly growing modern-day issue. Traffic congestion causing hour-long stagnation in traffic flows is becoming a daily occurrence which has resulted in a notable amount of research in the field in recent years aimed at developing algorithms for accurate long-term forecasts of traffic flow. This is critical for the development of intelligent transport systems, better urban planning, and punctual release of traffic congestion among numerous other applications. This paper provides a comprehensive survey of various optimization algorithms that are in use to solve the traffic flow problem. At first, we introduce the fundamentals of traffic flow prediction, followed by an overview of data sources and data collection methods. Next, we summarize recently developed predictive models and explain their strengths and limitations. We then discuss various factors affecting traffic flow predictions such as population growth, urbanization, etc. Finally, we present case studies and point out various challenges and future directions of long-term traffic prediction.