The dominant trajectory of artificial intelligence development has been characterized by scaling: more parameters, more data, more compute. Yet a persistent and widening gap remains between what these systems can compute and what civilization-scale problems actually require. Complex systems — cities, food networks, freshwater infrastructure, air-quality ecosystems — do not yield to statistical pattern-matching. They demand causal reasoning, physical grounding, adaptive evolution, and an understanding of constraint that emerges from the structure of reality itself, not from the structure of a training corpus. This paper presents the CES-QN (Causal–Self-Evolving–Quantum-Neuromorphic) Framework: a theoretical architecture for a class of intelligence systems designed to operate on finite-scale, real-world NP-hard optimization problems in sustainability-critical domains. The framework integrates four interdependent pillars — (A) a Causal-Reasoning Engine built on Structural Causal Models, (B) an Embodied Neuromorphic Loop grounded in spiking-neuron hardware, (C) a Self-Evolving Meta-Learning engine capable of controlled runtime self-modification, and (D) a Quantum-Accelerated Optimization layer exploiting Hilbert-space geometry for NP-hard problem classes. The architecture is not proposed as a definitive pathway to Artificial General Intelligence (AGI) or Artificial Superintelligence (ASI), but rather as a potential architectural framework for addressing finite, high-dimensional, multi-objective optimization problems that exceed the practical limitations of current machine learning and agentic AI systems. The CES-QN framework is intended as an exploratory systems-level standard integrating causal inference, adaptive neuromorphic learning, and quantum-inspired optimization under known mathematical and physical constraints. At the same time, the framework acknowledges that contemporary computational paradigms remain fundamentally bounded by established principles in mathematics, computational complexity, and quantum information theory — including Gödelian incompleteness, NP-hardness, the no-cloning theorem, and uncertainty constraints within quantum-state evolution. Future advances in physics, information theory, or computational architectures may expand these boundaries; however, the present work remains focused on scientifically grounded, finite-scale complexity and sustainability-oriented intelligence systems. Two primary validation domains are proposed: water safety and atmospheric pollution management, with future extensions to food security, urban-rural planning, bio-photonics, defence and medical systems. A formal comparison with classical machine learning, large language model pipelines, and modern agentic AI demonstrates the structural advantages of the CES-QN approach across causal reasoning depth, energy efficiency, adaptability, and alignment with universal physical principles. The paper is offered as a foundation document for timestamped preprint archiving and subsequent extension to journal publication.