Traditional scheduling theory assumes deterministic or independently random process behavior, failing to capture the temporal patterns in real systems. This work presents a predictive framework that models processes as discrete-time Markov chains with learnable behavioral states and treats resource heterogeneity as an optimization opportunity. The central contribution is the EWMA-Markov predictor, combining exponentially weighted averaging with Markovian modeling through \(P_{ij}(t+1) = \alpha P_{ij}(t) + (1-\alpha )\mathbb {I}[\text {transition}]\) , achieving O(1) complexity and numerical stability. For convex cost functions, we prove the cost under predictive scheduling satisfies \(\text {Cost}_{\text {predictive}} \le \text {Cost}_{\text {reactive}}(1 - D(\mathcal {P})H(\mathcal {R})/(1+\kappa ))\) , where \(D(\mathcal {P})\) measures process diversity, \(H(\mathcal {R})\) resource heterogeneity, and \(\kappa \) switching overhead. This quantifies when heterogeneous systems outperform homogeneous ones. Robustness analysis shows prediction errors cause only linear degradation: \(\text {Cost}_{\text {achieved}} \le \text {Cost}_{\text {optimal}} + 2\varepsilon \cdot \text {diameter}(\rho )\) for error \(\varepsilon \) . The framework maintains fairness with bounded distortion and integrates into production schedulers. While validated on CPU scheduling, the mathematical structure generalizes to any domain with stochastic demand and heterogeneous supply.