In many situations, when field researchers are inclined to deviate from the preselected sample plan and then to include nearby or related units in the sample, adaptive cluster sampling provides a nearby complete solution. For both rare and clustered populations, Thompson has introduced the adaptive cluster sampling (ACS) as a suitable sampling method when the data is not contaminated with outliers. So keeping this thing in mind, the present study focusses on defining adaptive ratio type regression estimators using OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. Subsequently, we have proposed regression type estimators utilizing OLS, Huber M, Mallows GM, Schweppe GM and SIS GM estimation functions within the framework of ACS. In this study we have also derived the mean square error property of both the adapted and proposed estimators. To evaluate the performance of these estimators we have used both real life and simulated Poisson clustered process data sets.