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Quantitative preclinical imaging enables non-invasive characterization of physiological, molecular, and functional processes across a variety of experimental models, providing metrics that inform longitudinal studies and translational research. This review synthe-sizes current strategies for quantitative imaging across modalities including Positron emission tomography (PET), Single Photon Emission Computed Tomography (SPECT), Magnetic resonance imaging (MRI), Computed Tomography (CT), optical imaging, and hybrid systems. We examine methodological frameworks for parameter extraction, re-producibility, and validation against biological reference standards, highlighting the strengths and limitations inherent to each modality. Key challenges, such as protocol harmonization, cross-platform comparability, and integration across species, are ana-lyzed, alongside computational advances including parametric mapping, and artificial intelligence–assisted pipelines. Emerging approaches that combine multimodal acqui-sition with standardized reconstruction and calibration strategies are also discussed, emphasizing their potential to enhance precision, reduce bias, and support biologically meaningful interpretation. Collectively, this review provides a comprehensive per-spective on the design, implementation, and validation of quantitative preclinical im-aging studies, offering practical guidance for generating reproducible, interpretable, and translationally relevant imaging biomarkers. By integrating methodological insights with advances in technology and analytics, it underscores the role of quantitative frameworks in bridging preclinical discovery with translational applications.