Waste stabilisation ponds (WSPs), also referred to as wastewater bio-ponds, are widely used for low-cost wastewater treatment, but their performance is often difficult to assess due to the coupled influence of biological activity, water quality, pond geometry, hydraulic behaviour, and site-specific operating conditions. This study proposes a practical first-order diagnostic framework for supporting the assessment of WSP systems, particularly where conventional monitoring infrastructure is limited. The framework combines a systems-level understanding of biological, chemical, and physical pond processes with targeted water quality monitoring, field profile measurements, and image-based surface-flow characterisation. Particular attention is given to flow behaviour, since it influences hydraulic residence time, spatial variability, and treatment efficiency. A homography-based image processing workflow is introduced to transform recorded surface motion into a metric reference frame, allowing surface velocity vectors to be estimated from tracked flow features and used as indicative hydraulic information. Because no independent reference-flow measurement was available, the image-derived velocities and discharge estimates are presented as preliminary and indicative first-order hydraulic information for interpreting flow behaviour and approximate discharge conditions, rather than as standalone or quantitatively validated flow measurements. The framework was demonstrated using one surface-flow case study and a single water-quality monitoring campaign at the Woodlands Hills three-pond WSP system, together with a second surface-flow case study at an agricultural runoff channel connected to the Moses River. The results provide time-specific diagnostic insight into hydraulic behaviour and treatment response, but do not constitute replicated, seasonal, or generally representative evidence of WSP performance. The proposed framework therefore provides a practical basis for assessing existing WSP systems, identifying likely performance limitations, and guiding future validation and optimisation work.