Additive friction stir deposition (AFSD) is a solid-state manufacturing technique capable of producing high-strength, defect-free metal components. The complexity of its process parameters has driven growing interest in machine learning (ML) for improved predictive accuracy and process control. This study presents a novel biomimetic ML approach to predict the mechanical properties of AFSD-fabricated aluminum alloy-walled structures. The methodology integrates numerical modeling of the AFSD process with genetic algorithm (GA)-optimized ML models to predict von Mises stress and logarithmic strain. Finite element analysis was employed to simulate the AFSD process for five aluminum alloys: AA2024, AA5083, AA5086, AA7075, and AA6061, capturing the complex thermal and mechanical interactions involved. A dataset of 200 samples was generated from these simulations. Decision tree and random forest (RF) regression models, optimized using GAs, were developed to predict key mechanical properties. The RF model demonstrated superior performance, achieving R² values of 0.9676 for von Mises stress and 0.7201 for logarithmic strain. This innovative approach provides a robust tool for understanding and optimizing the AFSD process across a range of aluminum alloys, offering valuable insights into material behavior under various process parameters.