Our study addresses the urgent need for effective detection of Klebsiella pneumoniae, a recognized threat for global health by the World Health Organization (WHO). Current challenges in managing K. pneumoniae infections include the lack of rapid and affordable detection tools, particularly in resource-limited point-of-care (POC) settings. To tackle this, we developed an innovative molecular detection pipeline combining three POC-compatible methods. Firstly, we employed Insta DNA™ card-based sample collection and DNA extraction for simplicity and ease of use. Next, we utilized Recombinase polymerase amplification (RPA) targeting the Klebsiella hemolysin gene, khe, specific to the K. pneumoniae species complex (KpSC). Finally, we integrated a silver nanoparticle (AgNP) aggregation assay for visual detection, offering a rapid, sensitive, and specific method capable of detecting as few as ~3 bacteria of K. pneumoniae within ~45 minutes. This approach eliminates the need for complex equipment, making it highly suitable for field and resource-limited POC applications. Moreover, our method introduces an environmentally significant detection strategy. The method developed minimizes chemical reagent usage and reduces the carbon footprint associated with sample transportation. Furthermore, our method reduces waste compared to the traditional detection techniques, offering a safer alternative to ethidium bromide or other DNA dyes which are often genotoxic and mutagenic in nature. Silver nanoparticles, being environmentally safer, can also be recycled from the waste, contributing to sustainability in nanoparticle production and disposal. Overall, our technique presents a promising solution for detecting K. pneumoniae in various settings, including environmental, water, and food samples, as well as industrial or hospital effluents. Aligned with global efforts to enhance public health and sustainability, our approach holds potential for improving disease management and reducing the ecological footprint of rapid diagnostics in resource-limited settings.