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In the recent era, Transportation considers the most powerful component of the business logistics system. Likewise, there is an interdependent relationship between the transportation and logistics systems. This paper aims to make a comparative study of logistics transportation problems based on intelligence algorithms. The researchers surveyed the previous studies conducted in the Artificial Intelligent field to solve complex problems. In this research study, the authors focused on techniques that are mostly applied in transportation and logistics systems, especially, Artificial Neural Network, Genetic Algorithm, and Fuzzy Logic models. Also, a proposed model and algorithm was done to obtain customers' and organizations' satisfaction. Artificial Neural Network uses as a decision tool that combines the system stat sets and the operation state-dependent sets. As well, the genetic algorithm combines the best parameters as a method to finds the best evaluation solutions. And fuzzy logic uses a fuzzy set to help decision-makers in making the best decisions in multiple fields. Finally, authors recommended to work in two new areas which are FGA, NFGA Algorithms to solve complex and multimodal problems that faces transportation logistics sector. [ABSTRACT FROM AUTHOR]