Respiratory diseases continue to ravage the world, and their effects remain pronounced in Nigeria. This research has been able to create a predictive model for safety measures of respiratory diseases taking a case study of the COVID-19 disease. It identified, collected, and examined data on individual cases; designed a predictive model using the data; implemented the designed model; and evaluated the performance of the system. The required data samples of COVID-19 infected cases needed to train the model were collected from a domain expert. The collected samples were examined and analyzed based on the variation of symptoms presented in terms of age, weight, and underlying diseases. The correlation analysis was done using Pearson's correlation coefficient to determine the variables that influence the disease. A predictive model was implemented using Adaptive Neuro-fuzzy Inference System in Python programming language. A total of 4000 dataset were employed where 20% was randomly used for the evaluation process. The model achieved a MSE score of 0.084, MAE score of 0.079, and a Normalized Mean Squared Error of 0.103. The result obtained showed that the system is capable of predicting the risk level and recommends the appropriate agency for further examination and management by the medical practitioners.