To address the challenges posed by climate change and the demand for sustainable production, robust and efficient bioprocesses are alternatives to conventional chemical processes. However, many promising bioprocesses that perform well at laboratory scale fail upon scale-up to industrial scale. This discrepancy among other arises from environmental gradients in large scale bioreactors causing phenotypic population heterogeneity, metabolic burden, and genetic instability. Traditional assumptions of homogeneous cell behavior under well-mixed conditions are challenged by complex intracellular networks and spatial-temporal environmental variations, necessitating a multi-level bioprocess monitoring approach. This chapter reviews bioprocess monitoring strategies across hierarchical cell-physiological levels and discusses physical, chemical, and biological parameters critical for bioprocess understanding and control. It highlights measurement techniques, from in-line sensors enabling real-time control to off-line comprehensive analyses, and contrasts laboratory-scale flexible, multi-level monitoring with industrial-scale demands for robust, cost-effective, and regulative-compliant systems. Bridging the monitoring gap between scales involves applying “beginning-with-the-end-in-mind” in bioprocess design, integrating advanced monitoring such as single-cell analysis, scalable in-line sensors, and multi-level bioprocess modeling. Combining experimental data with computational models predicts scale-dependent cellular behavior, guides bioprocess optimization, and enables soft sensor development for real-time, automated closed-loop industrial bioprocess control. This integrated framework promises improved scale-up and enhanced robustness in industrial-scale bioprocesses.