Macrocyclic peptides can address challenging therapeutic targets that are often poorly modulated by conventional small molecules or biologics, but their design is difficult because conformational preorganization, membrane permeability, and target binding are interdependent and sometimes competing requirements. Existing generative approaches for peptides often focus on sequence novelty or target affinity without fully accounting for the geometric constraints imposed by cyclization, highlighting the need for models that reason jointly over topology, conformation, and medicinal chemistry properties. This article proposes a diffusion-based generative framework for designing macrocyclic peptides conditioned on predicted conformational stability, membrane permeability, and binding affinity, enabling the generation of candidate macrocycles that satisfy multiple design criteria within a single generative process. The model operates on cyclization-aware three-dimensional coordinates or torsion-angle representations, with property predictors guiding denoising toward molecular structures that exhibit favorable therapeutic profiles. Conceptually, this approach can produce synthetically plausible macrocyclic peptides with desirable permeability, target engagement, and conformational preferences, which should then be evaluated through computational filters, molecular dynamics simulations, and prospective experimental testing before being considered as drug leads. By integrating structural generation with pharmacokinetic and pharmacodynamic constraints, multi-constraint diffusion generation offers a model-oriented strategy for efficiently exploring drug-like macrocyclic peptide chemical space and accelerating the rational design of constrained peptide therapeutics.