Tablet compression is a critical manufacturing step in which raw materials, granule attributes, and process settings converge to determine the quality of the final solid dosage form. Compression behavior influences mechanical strength, dissolution, content uniformity, and manufacturability. Traditional Quality-by-Design workflows often rely on univariate interpretation, static multivariate models, or limited design-of-experiments outputs. This leaves unrealized value in the rich spectral, granule, and process datasets now generated by modern manufacturing systems. This article proposes an AI-enabled QbD workflow that integrates NIR and Raman spectral data, granule physical properties, and tablet press parameters into a unified predictive and optimization framework. The aim is to support conceptual design-space development, process understanding, and real-time quality decision-making. The proposed workflow uses data fusion to align spectral, granule, and compression data into model-ready representations. Gradient-boosted models, neural networks, and chemometric baselines are positioned as complementary tools for predicting hardness, dissolution, and content uniformity within a lifecycle-oriented QbD structure. Conceptually, the workflow could identify suitable compression settings for each granule batch, anticipate quality deviations, and support adaptive model maintenance as new manufacturing data become available. It would be expected to transform fragmented PAT measurements into a coherent quality intelligence layer. An AI-enhanced QbD workflow could accelerate tablet process development, reduce avoidable batch failures, and strengthen the scientific basis for real-time release testing. Its value depends on transparent model governance, validated analytical data, and practical integration with manufacturing control systems.