The increasing complexity of software-intensive systems has created a need for scalable reasoning mechanisms capable of identifying interacting constraints rather than evaluating isolated performance conditions. Conventional optimization approaches frequently treat constraints independently, although real-world systems exhibit dependencies among computational, architectural, resource, and operational variables. This paper proposes CombiScale, a conceptual large language model (LLM) framework for scalable constraint reasoning and optimization. The framework combines language-based semantic interpretation, dependency-aware constraint representation, combinatorial reasoning, risk propagation, optimization-oriented recommendation, and explainable decision support. Its theoretical foundation is informed by the combinatorial LLM approach presented in ScalePulse, which demonstrates the value of jointly analyzing software artifacts and architectural relationships for scalability assessment (Ramamurthy, Bellamkonda and Amanmadov, 2026). The proposed framework extends this reasoning paradigm by treating scalability optimization as a multi-constraint decision problem rather than solely as anomaly detection. The supplied literature also demonstrates the importance of structured sensing, resource allocation, environmental monitoring, and multidimensional system analysis, providing transferable principles for constraint-aware optimization (Alizadeh & Soltani, 2016; Almagrabi, 2020; Cao et al., 2022; Chaudhri et al., 2022). The resulting framework is organized into six functional layers: constraint extraction, semantic normalization, dependency graph construction, combinatorial reasoning, optimization, and explainability. Analytical findings indicate that the principal value of CombiScale lies in connecting local constraints with their systemic consequences, enabling scalable reasoning across heterogeneous system representations. The framework nevertheless remains constrained by LLM inference cost, dependency-quality requirements, uncertainty propagation, and the absence of a universal optimization objective. Future validation should therefore combine controlled benchmark datasets with real-world workload and architectural telemetry.