Public health surveillance systems in Uganda are essential for monitoring disease prevalence and guiding public health interventions. However, their effectiveness varies, necessitating methodological evaluation to improve data quality and yield improvements. A mixed-method approach was employed, combining quantitative analysis with qualitative interviews. Time-series forecasting models were applied to historical data from two major infectious diseases (malaria and tuberculosis) to predict future trends and yield improvements. The time-series model showed an R² value of 0.75 for malaria surveillance and 0.68 for tuberculosis, indicating a moderate level of fit. Interviews revealed that system reliability was compromised by inconsistent data reporting across regions. The models provided reliable predictions but were undermined by regional disparities in data quality. Recommendations include strengthening data collection protocols and enhancing inter-regional collaboration to improve surveillance accuracy. Integrate robust data validation mechanisms, encourage standardised reporting practices, and foster a collaborative environment among public health agencies for improved system performance. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.