Statistical channel models play a key role in the design and standardization of next-generation wireless systems. The models substitute for costly and time-consuming field measurements when fitted distributions accurately reproduce the measured channel impulse responses. Future wireless systems will operate in global spectrum allocations from 1 to 150 GHz. Over the full span, analytically chosen distribution families fitted to scarce measurement data fail to deliver the required accuracy. Generative AI models instead learn each distribution from the radio-propagation data itself, adapt to the measured structure at every frequency, and scale across the entire band. We present ARIA, an AI-powered Radio-channel Inference and Analysis pipeline that learns each channel-parameter distribution from the measurements. The pipeline extracts power-delay profiles and recovers the channel parameters with time-cluster spatial-lobe (TCSL) analysis. Three neural density estimators, namely mixture density networks, neural spline flows (NSF), and denoising diffusion implicit models, learn each parameter's distribution. A composite rank across goodness-of-fit metrics with Friedman-Nemenyi testing identifies the best estimator, and a maximum-likelihood step anchored to the measured samples converts its density into closed-form parametric families. We instantiate ARIA on the NYU WIRELESS 16.95 GHz InH campaign and find that NSF outperforms the other estimators on every metric. We also show that a conditional NSF with feature-wise linear modulation, trained jointly on the 6.75 and 16.95 GHz measurements, improves over single-frequency training. We feed the fitted distributions into the NYUSIM simulator and reproduce the measured omnidirectional RMS delay spread medians within 2 ns, the per-lobe azimuth and zenith spread medians within 1 degree, and the omnidirectional zenith spread medians within 0.5 degrees.

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