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An integrated linear regression technique was developed and validated to model dissolved oxygen in salt lakes by using R software and based on data from Sawa Lake, Iraq. The technique helps understand and evaluate salty aquatic ecosystems in the presence of water quality data gaps. The technique involved selecting the important water quality parameters that have significant statistical relationship with dissolved oxygen. In order to make the regression development simpler, the validation approach was incorporated with the model. Linearity, homogeneity, normality, outliers, and influential data points were verified. The simulation approach was also capable of displaying the interaction between the selected water quality parameters and the other insignificant parameters. The statistical analysis results indicated that dissolved oxygen in salt lakes is a function of total dissolved solids. The developed model represented dissolved oxygen with R-squared of 90.73% and p-value of 1.08E-06. Furthermore, the model results showed that the influence of salty ions on dissolved oxygen/total dissolved solids model is the same. It was found that temperature has a significant impact on the developed dissolved oxygen model. In addition, the model simulation revealed that salt melting surrounding the lake due to temperature variation during the year cycle increased total dissolved solids in the lake water. Consequently, the lake dissolved oxygen levels were impacted.