Due to advancements in computing systems, intelligent transportation networks, and communication technologies, the development of VANETs is possible. Traffic prediction in high mobility and dynamic network topologies is highly challenging in VANETs. It is very important for high accuracy in traffic prediction as it reduces congestion, provides smooth flow of traffic, and ensures road safety. This chapter is going to discuss how deep learning technologies can be used in solving the problems above. It gives importance to different methodologies, architectures, and practical implementations. Deep learning is one of the subsets of machine learning that has gained much popularity due to its excellent ability to predict complex relationships and patterns in large datasets. It checks applicability to the problem of traffic-patterns forecasting and solving time series problems using CNN, RNN, and LSTM. This chapter refers to strategies on data preparation-data cleaning, normalization, and augmentation. Data preparation with its key demands-normalization, augmentation makes the input data correct for the deep learning model application; thus, correctness also relies on relevance. A list of relevant topics incorporates federated learning and transfer learning. Even though it doesn’t solve the problem of sparse data, transfer learning helps with the adaptation and adoption of models developed from big sets of data to new and related settings. In federated learning, instead, diversity within centralised collections of data is increased because these models decentralised work to perform computations but differ by their levels of privacy. This comprehensive review of deep learning algorithms for traffic prediction in VANETs can provide much-needed insight to academics, practitioners, and stakeholders interested in intelligent transportation systems.