<p>Tourism encompasses intentional journeys to locations where individuals do not typically reside or work to pause their routine for relaxation, exhilaration, relish, or conducting business. However, the increasing number of tourists presents substantial challenges to the availability of tourist facilities, sustainability, integrity and biodiversity of destinations. The concept of tourist carrying capacity (TCC) is implemented to address these difficulties. TCC integrates comprehensive factors such as visitor management, infrastructure, facilities, and environmental factors. The tourist capacity should focus on accepting the tourist requirements, absorbing them into a destination, and rejecting overcapacity to maintain sustainability for the positive and negative impact of tourism. However, in TCC, the parameters of tourist support facilities are uncertain. Hence, this paper implements the algorithm to solve a multi-objective linear programming problem (MOLPP) through an intuitionistic fuzzy multi-objective (IFMO) approach for determining novel applications in TCC. In this study, the intuitionistic fuzzy sets are used to solve the uncertainty and impreciseness in the objective functions and their constraints in the novel TCC problem that accepts and rejects tourist flow to prevent degradation of the environment. The objective of the problem is to maximize the total outlay of each category of visitors and minimize waste disposal, subject to constraints related to tourist facilities. To accomplish this, the intuitionistic fuzzy deterministic parameters turn into crisp parameters. Then the algorithm constructs and formulates membership and non-membership functions under the degree of acceptance and degree of rejection of objectives functions and constraints simultaneously. Additionally, the sensitivity of the uncertain parameters was evaluated in relation to various scaling factors of the IFMO approach. The results demonstrate that the scaling factors significantly influence the model’s performance, indicating the different values of TCC to optimize the outcomes. Moreover, an illustration analyses the TCC of Venice, a historical city and shows the comparison with the existing fuzzy MOLP technique; the proposed model shows the independence and dependency of the type of visitors related to the transport system, waste disposal, meals, and room availability. The results show that intuitionistic fuzzy multi-objective tourist carrying capacity problem (IFMOTCCP) provide more satisfactory optimal results to validate the proposed algorithm.</p>