aspg-34-3650-1791485082 20261008184442 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Prospects for Applied Mathematics and Data Analysis PAMDA 2836-4449 2024 4 1 Sawsan Rateb almokabaa Master's Student - Faculty of Science, Damascus University – Damascus, Syria Maissam Ahamad Jdid Faculty of Science, Damascus University, Damascus, Syria The efforts of many researchers and scholars have focused on providing appropriate algorithms for generating random numbers and developing them in a manner that suits the need for them, but these algorithms still have advantages and disadvantages, so they are suitable for a specific study and not suitable for another study. The reason for the interest of researchers and scholars in the process of generating random numbers is that random numbers have many scientific and technical applications, starting with generating a series of semi-random numbers, from computer simulation to encryption, games of chance, and random samples for statistics and security. In simulation, which is one of the important methods provided by the new science of operations research, the primary reliance is on generating a series of random numbers that follow the regular distribution in the range [0,1], and then converting these random numbers into random variables that follow the probability distribution according to which the system to be simulated works, as the accuracy of the results we obtain from the simulation process depends on the numbers we generate using one of the algorithms. In other words, the appropriate algorithm for the field of study must be chosen from among the algorithms used, which prompted us to prepare this research. We present a reference study of some of the algorithms used to generate random numbers, highlighting their advantages, disadvantages, and most important areas of use. We then calculate and compare these algorithms. The algorithms discussed are the Middle Square Method, Middle multi-Method, Fibonacci Methods, and Linear congruential Methods. 2024 09 17 3650 https://creativecommons.org/licenses/by/4.0/ 10.54216/PAMDA.040102 https://www.americaspg.com/journal/34/article/3650 https://www.americaspg.com/storage/articles/manuscripts/01M1PMDRT302HX3Y019X50Z1AF.pdf