With the development of computer technology, its usefulness has been discovered in many scientific fields such as pharmacy, materials science, agriculture and organic chemistry. Traditional laboratory synthesis of molecules is by itself long and exhaustive, and can be also financially demanding, with the biggest drawback being the quantity of synthesized product. Conducting classical laboratory syntheses demands exquisite knowledge and expertise in organic chemistry. This paper explores the integration of molecular modeling with artificial intelligence in order to manufacture and optimize new synthetic pathways in organic chemistry. Technological advancements by computer scientists have enabled chemists to perform molecular modeling on computers, and with the support of artificial intelligence, have facilitated the discovery of new synthetic routes that even scientists themselves might not have thought of. Furthermore, during the presentation of results computers with trained software programs have ability to display reaction yield percentages. Thanks to this, chemists can determine whether a synthetic route is worthwhile or if they should continue searching for a new one, followed by the conformation of rejection of the results provided by the computer. Molecular modeling alone provides insight into the chemical structure of molecules, their physical and chemical properties, biological activity, reactivity and many other aspects. In contrast, artificial intelligence, especially machine learning approaches assesses which data from the database are high quality and uses those combinations to further the synthetic pathway. By combining these technologies, there has been a significant progress sin organic synthesis, particularly in terms of saving time and cost, and bench chemists do not need to fear what results will reaction yield give.