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New features, design suggestions, and solutions for the next generation of cellular systems need to be tested in a variety of real-world deployments and use cases. 3GPP-compliant 3GPPcompliant system-level holistic and realistic simulators are urgently needed to evaluate the variety of AI-based network automation solutions that are being suggested in the literature. The Synthetic-NET simulator created at AI4networks Lab is presented in this publication. A python-based emulator that completely complies with 3GPP 5G standards update 15 and can be upgraded to future releases called Synthetic-NET, according to the authors. In comparison to other simulators, Synthetic-NET has a number of key advantages, including: 1) a modular design that makes it easier to cross-validate and upgrade to future releases; 2) a variety of propagation modelling options, including measurement-based, ray-tracing-based, and AI-based models; 3) the ability to import data sheets based on measurements of realistic base  station features, including such satellite and energy consumption patterns; and 4) sui generis support for a wide range of wireless protocols. The Synthetic-NET's ability to be utilised to test AI-based network automation solutions is another important feature of the product. Synthetic Net’s built-in abilities to analyze and process large amounts of data and integrated access to Applications Of machine learning  contribute to this simplicity of use, which is the first python-based 5G emulator. A powerful platform for both academia and industry alike, the Synthetic-NET simulator is a powerful tool for experimenting of not only creative approaches for optimising the operation of both existing as well as starting to emerge wireless connections but also for developing AI-powered deep mechanisation in the years to come.