The study explores turbulent forced convection of hybrid nanofluids in a three-dimensional L-shaped channel using COMSOL Multiphysics. The hybrid nanofluid comprises copper and aluminum oxide particles dispersed in water, with volume fractions of 0.01 (1%), 0.04 (4%), 0.07 (7%), and 0.1 (10%). Simulations are conducted for forced convection under Reynolds numbers ranging from 10,000 to 40,000. The partial differential equations of the Navier-Stokes, model of the turbulence, and the three-dimensional energy equation are employed to model the system. The turbulence kinetic energy and dissipation rates for the turbulent flow range from 2.99E-3 to 1.53 ( ) and from 0.2136 to 2524.1913 ( ), respectively. This research is significant for advancing efficient heat transfer mechanisms, which are crucial for applications in cooling systems and energy devices. Key findings include the observation that the minimum temperature at the channel edges decreases with an increasing volume fraction of aluminum oxide and improves with copper addition. Copper consistently enhances the minimum temperature across all scenarios. The Nusselt number calculated using aluminum oxide is nearly ten times greater than that obtained using copper. The friction factor initially increases along the channel length and then decreases, showing minimal sensitivity to variations in the volume fractions of copper and aluminum oxide. Using multiple linear regression, predictive equations for the average temperature and Nusselt number at the outlet were developed based on the Reynolds number and the nanomaterial volume fractions. The novelty of this work lies in the exploration of a three-dimensional L-shaped channel to analyze its thermal behavior, offering significant insights for engineering applications and design improvements. Furthermore, the study provides a novel contribution by deriving linear regression equations for the average Nusselt number and average temperature, which have not been addressed in previous literature. The absolute error for the average temperature equation ranges from 0.005 to 1.6, while for the Nusselt number, it varies between 0.368 and 1.6, ensuring high accuracy of the regression model.