GPU kernel optimization is essential for highperformance machine learning systems, but it remains laborintensive. Triton simplifies GPU kernel development with a Python-based interface, yet autotuning still requires searching a large configuration space, which is costly. We present TTX, a Triton tuning framework with an XGBoost-based performance predictor. By using input shapes, tuning parameters, and IR-level features, TTX accurately estimates kernel latency and quickly selects promising candidates for further search. Experiments show that TTX achieves around $10 \%$ MAPE on operators such as matrix multiplication, batched matrix multiplication, and convolution across multiple GPUs. It reaches about $80 \%$ of the best performance with the top-1 candidate and over $95 \%$ with the top- 50 candidates.