Brain aneurysms impact 3-5% of the population, with a 1% annual rupture risk causing severe outcomes. Treatments like clipping, coil embolization, or flow-diverter stents often fail in non-surgical cases due to poor conformity, coverage, and recurrence from limited compressibility. We present an auxetic Nitinol stent that fills and conforms to aneurysm irregularities. Auxetic properties (negative Poisson's ratio) enable better expansion. Patient-specific optimization uses Bayesian methods on a Gaussian Process Regression (GPR) metamodel to cut computational costs. Workflow: Python LHS samples initial designs; Abaqus finite element analysis evaluates auxetic cells under compression. GPR surrogate trains on data; Bayesian optimization (e.g., expected improvement) proposes, evaluates, and refines iteratively, balancing conformity, coverage, and integrity. Anti-tetrachiral pattern parameterized by α, β, γ minimizes stress, targets negative Poisson's ratio, maximizes area ratio. At 30% compression, Pareto front yields optimum: α=0.13128, β=0.10140, γ=0.7. Bayesian framework accelerates personalized designs, improving aneurysm treatment efficacy and reducing risks.