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Antimicrobial resistance (AMR) is a major global health crisis, particularly due to multidrug-resistant gram-negative bacteria that are difficult to treat because of their impermeable outer membrane and strong efflux mechanisms, making antimicrobial peptides (AMPs) a promising alternative owing to their broad-spectrum activity and rapid bactericidal effects, although their clinical translation is limited by instability, production costs, and toxicity to human cells; meanwhile, traditional experimental discovery of AMPs is slow and expensive, and existing computational methods often optimize only antimicrobial activity while neglecting toxicity or diversity, leading to unsafe or narrow solutions, while reinforcement learning approaches may suffer from mode collapse and limited exploration of sequence space; to address these limitations, this work proposes a generative flow networks (GFlowNets)-based framework for multi-objective AMP design against gram-negative pathogens, in which a sequence generator constructs peptides stepwise and is guided by a reward function that integrates predicted antimicrobial activity and human cell toxicity from separate machine learning models, enabling simultaneous optimization of efficacy and safety; unlike conventional generative models, GFlowNets sample sequences proportional to reward, promoting diverse outputs that span the Pareto frontier of activity-toxicity trade-offs, while also allowing conditioning on desired properties and modular improvement of predictive components over time; overall, this framework provides a principled and scalable approach to antimicrobial peptide discovery that balances potency, safety, and diversity, potentially accelerating the identification of therapeutic candidates for combating antimicrobial resistance.