###### Abstract  
The primary goal of this research is to present a novel computational intelligence approach of the AI-based Levenberg-Marquardt scheme under the influence of backpropagated neural network (LMS-BPNN) for optimizing MHD ternary hybrid nanofluid using Casson fluid over a porous shrinking sheet in the existence of thermal radiation (\(Rd\)) effects. The governing partial differential equations (PDEs) showing the Casson ternary hybrid nanofluid are converted into a system of ordinary differential equations (ODEs) with suitable transformations. The numerical data is constructed as a reference with bvp4c (MATLAB built-in function used to solve a system of ODEs) by varying Casson fluid parameters (\(\beta\)), magnetic field (\(M\)), porosity (\(S\)), nanoparticle concentrations (\(\phi_{1}=\phi_{2}=\phi_{3}\)), and thermal radiation (\(Rd\)) effects across all LMS-BPNN scenarios. The numerical data-sheet is divided into 80% of training, 10% of testing, and 10% of validation for LMS-BPNN are used to analyze the estimated solution and its assessment with a numerical solution using bvp4c is discussed. The efficiency and consistency of LMS-BPNN are confirmed via mean squared error (MSE) based fitness curves, regression analysis, correlation index (\(R\)) and error histogram. The results show that velocity decreases as \(\beta\) grows, whereas velocity increase as \(M\) increases. The concentrations of nanoparticles and thermal radiations have increasing effects on \(\theta(0)\). To comprehend the dependability and correctness of the data gained from numerical simulations, error analysis is a key stage in every scientific inquiry. Error analysis is presented in terms of absolute error and it is noticed that the error between the numerical values and predicted values with AI is approximately \(10^{-6}\). The error analysis reveals that the developed AI algorithm is consistent and reliable. C 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). occurrence in nature. The unsteady Casson fluid was examined by [PERSON] and [PERSON] [7] and they concluded that fluid velocity increases by increasing \(\beta\), which is a Casson fluid parameter. [PERSON] et. al. [8] scrutinized the flow behaviour of non-Newtonian fluid over the non-porous wedge and concluded that \(Nu_{x}\) shows an increment with an increasing Casson parameter and thermal radiation effect. [PERSON] and [PERSON] [9] investigated boundary layer flow over a porous wedge and the effects of \(\beta\) are discussed in flow situations. They observed that when values of \(\beta\) are increased then \(Cf_{x}\) is increased. Nanofluids are fluids that are enhanced with nanoparticles. The nanoparticles, which are typically made of metals or metal oxides are dispersed in fluids typically a liquid such as water, to form a suspension. The addition of nanoparticles to a fluid has been shown to improve its thermal conductivity (\(Rd\)), making it a promising material for use in heat transport phenomena and applications such as cooling in electronics and automotive systems. Nanofluids have also been studied for their potential use in other applications, including biomedical imaging and drug delivery. However, more research is required to fully understand the characteristics and transport properties of nanofluids and their potential ability for real-world applications. Additionally, there are apprehensions about the safety and environmental impact of nanoparticles, which must be carefully considered before nanofluids are widely used.