In the present paper, a case study of selected Indian Pulp and Paper mills is discussed where the aim is to measure the efficiency of the mills based on the available data containing information about the 32 pulp and paper mills as per the 6 selected criteria. To calculate the efficiency of each Mill, data envelopment analysis (DEA) is an appropriate technique. However, classical DEA models often neglect the presence of undesirable outputs or inputs that need to be minimized or reduced. This study addresses this limitation by developing a two-stage network DEA framework that contains undesirable variables. A two-stage network DEA is developed for evaluating the efficiency of decision-making units (DMUs) operating in a multi-stage process. Here, a DMU represents a Pulp and Paper mill. The results highlight the importance of considering undesirable variables in assessing the efficiency of DMUs accurately. The developed model offers valuable insights for decision-makers by identifying areas for improvement and suggesting strategies to enhance efficiency while dealing with undesirable variables. The efficiency scores obtained through the two-stage network DEA technique are compared with those calculated by the classical DEA method.