Small and medium-sized enterprises (SMEs) are vital to employment, supply chains and inclusive growth, yet their credit deterioration is often detected late in emerging-market banking systems. This paper develops an operational early-warning framework for identifying SME credit weakening before formal default or non-performing loan recognition. Using a framework-construction design grounded in recent credit-risk analytics, fintech lending, relationship-banking, explainable machine-learning and model-governance literature, the study synthesises borrower financial, account-conduct, relationship, sector-macroeconomic and network-fragility signals into a bank-usable architecture. The resulting EWS-SME framework converts heterogeneous deterioration signals into four watchlist bands: Green, Amber, Orange and Red. Each band is linked to predefined managerial actions, escalation timing, reason codes and monitoring responsibilities. The framework also specifies a validation protocol based on borrower-month panel data, out-of-time testing, imbalanced-class metrics, calibration, lead-time analysis, operational workload assessment, fairness review, override tracking and model-drift monitoring. The paper contributes by reframing SME credit monitoring as a deterioration-management process rather than a default-classification exercise. Its practical value is a staged implementation route that allows emerging-market banks to begin with reliable structured data and progressively incorporate soft information, textual notes, sector overlays and network variables. The framework is especially relevant for banks seeking earlier borrower engagement, more disciplined watchlist governance and stronger resilience in SME portfolios.