Composite T-joints consist of the skin, stiffener and flanges made of carbon fiber/epoxy laminate, which are joint by adhesive bonding. A long-standing problem with T-joints is cracking between the skins and flange due to the low strength and the presence of a large geometric stress concentration. The composite T joint structure has become a potential lightweight another option in aviation, aerospace, construction and vehicle engineering. The aim of this research is to apply bio-inspired optimization and experimental evaluation of composite t-joint to improve quasi-static mechanical properties. This study focused on tree branch-trunk joints and carbon fiber reinforced polymer (CFRP) T-joints using finite element analysis (FEA) and bio-inspired optimization techniques for the design and analysis to enhance structural performance. The study starts with a static structural analysis of tree branch-trunk joints using ANSYS 2022 Workbench to determine the maximum and minimum equivalent stress under applied loading conditions. The optimization of process parameters achieved through a combination of Artificial Neural Networks (ANN) and Multi-Objective Genetic Algorithms (MOGA). Basic findings indicate that FEA identified maximum bending stress of 109.60 MPa, tensile stress of 96.831 MPa and compressive stress of 54.769 MPa achieved at specific geometrical configurations. The ANN model demonstrated a high predictive capability with a mean absolute error of 0.311% confirming its suitability for this study. For the CFRP T-joint analysis optimization of the stiffener ply stacking sequence significantly improved mechanical properties. The bio-inspired design utilizing a stacking sequence of [45/0/-45/90/45/-45/0/90] achieved a 33.62% reduction in tensile stress and enhanced strength under various loading conditions. Experimental results indicated that the bio-inspired design improved bending strength by 15.7%, tensile strength by 16.2% and compressive strength by 16.49 % compared to the baseline design with the foam-embedded design exhibiting even greater strength improvements. The FEA results indicated in the bending load case it achieved decreases of 4.18% in normal stress, 56.31% in equivalent stress and 56.61% in maximum failure. The tensile load analysis revealed a 22.70% decrease in maximum failure for the Foam-Embedded design. Under compressive loads it achieved with decreases of 32.39% in equivalent stress and 66.29% in maximum failure. The Bio-inspired design also showed improvements particularly a 30.71% decrease in maximum failure. Overall the findings confirm that bio-inspired and foam-embedded design are effective in improving the strength and damage resistance of composite T-joints across bending, tensile and compressive loading conditions. Keywords: Nature-inspired optimization; Finite Element Analysis; Composite T-joint; Carbon Fiber Reinforced Polymer; Multi-Objective Genetic Algorithms; Artificial Neural Networks