In the post-COVID-19 era, the management of medical waste has become a precarious concern due to its potential risks to environment, patients, public and healthcare workers. Selecting the most suitable healthcare waste disposal technology (HWDT) is a complex task, complicated by multiple, often conflicting criteria. Optimization models are introduced to replace the traditional decision-making to encounter the limitations, such as weight distortion caused by defuzzification, uncertain nonlinear relationships of real-time models, and inadequate solution spaces exploration. This study introduces a novel hybrid SS-PSO algorithm to solve the optimization model for crisp weights directly from comparison matrices. The algorithm combines the strength of simplex search method (SSM) into particle swarm optimization (PSO). It is validated on three benchmark examples, achieving the lowest objective function values (0.2283, 4.4116, and 27.519) compared to existing methods, with enhanced consistency fitness. Later, to select the best HWDT, a multi-criteria decision-making (MCDM) technique called AHP-SS-PSO-TOPSIS (ASPT) is proposed. It integrates fuzzy analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) within a metaheuristic optimization framework. The integrated framework’s practical utility is demonstrated through a real case study in Indian subcontinent. The case study reveals that treatment effectiveness is the most important criteria with 21% weight. Autoclaving is the most appropriate technology for India to its low carbon emissions, energy efficiency, and cost-effectiveness. The sensitivity analysis has shown 91% stability on 21 distinct scenarios confirming the robustness and resilience of the ASPT method. The ranking results are compared with the other methods and similar rankings are obtained. Furthermore, the paper discusses the practical implications of this approach offering actionable insights for policymakers and healthcare administrators to adopt environmentally friendly and economically viable waste management technologies.