Scalability-constrained decision optimization requires decision systems to generate useful alternatives while simultaneously respecting computational, operational, and resource limitations. Conventional optimization approaches often depend on predefined candidate spaces, whereas generative large language models (LLMs) can construct semantically diverse decision alternatives but may produce infeasible or computationally expensive solutions. This paper proposes ScaleGen, a combinatorial generative LLM framework designed to integrate generative reasoning with explicit scalability constraints. The framework combines candidate generation, constraint representation, combinatorial evaluation, feasibility filtering, and final decision selection into a structured optimization pipeline. Its theoretical foundation is informed by research on machine learning, quantum machine learning, classification, and computational decision processes. Studies on quantum methods demonstrate the value of structured computational representations for complex learning problems (Ablayev et al., 2020a; Ablayev et al., 2020b), while quantum machine-learning applications illustrate the potential of advanced computational paradigms for classification and large decision spaces (Bhavsar et al., 2023; Yun et al., 2022). The proposed ScaleGen architecture extends these principles into an LLM-centered optimization setting by treating generated decisions as combinatorial objects subject to explicit scalability constraints. The framework emphasizes controllable generation rather than unrestricted text generation, thereby providing a conceptual bridge between generative AI and constrained optimization. The analysis indicates that ScaleGen can potentially improve candidate diversity, constraint awareness, and decision adaptability, although its effectiveness remains dependent on constraint encoding, evaluation reliability, computational overhead, and empirical validation.