This study presents a microfluidic impedance flow cytometry enabling the quantification of intrinsic single-cell bio-dielectric parameters (e.g., cell radius of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , relative membrane permittivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and cytoplasmic conductivity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> ) based on constrictional microchannel, numerical simulation, and neural network. Numerical simulation of a traveling cell within constrictional microchannel was conducted to locate relationship between intrinsic bio-dielectric parameters of single cells with corresponding impedance profiles, functioning as “reference” cells for the feedforward neural network, producing fitting accuracies of 1.00 ± 0.00 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , 1.00 ± 0.00 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and 1.00 ± 0.01 of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> . Then, the fitted analytical equation based on numerical simulation and neural network was further used to translate measured impedance profiles into intrinsic bio-dielectric parameters of single cells, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}_{\text {c}}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon _{\text {mem}}$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {cyto}}$ </tex-math></inline-formula> were quantified as 7.6 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.8 \mu \text{m}$ </tex-math></inline-formula> , 16.73 ± 2.97, and 1.38 ± 0.34 S/m of K562 cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=1284$ </tex-math></inline-formula> ), 6.4 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.8 \mu \text{m}$ </tex-math></inline-formula> , 17.47 ± 5.09, and 1.46 ± 0.47 S/m of Jurkat cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=2116$ </tex-math></inline-formula> ), 5.9 ± <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.7 \mu \text{m}$ </tex-math></inline-formula> , 17.40 ± 3.14, and 1.96 ± 0.81 S/m of HL-60 cells ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}_{\text {cell}}=3543$ </tex-math></inline-formula> ), respectively. Compared to phenomenal electrical signals (i.e., real and imaginary parts at specific frequency) detected by conventional electrical flow cytometry, which were prone to environmental variations, this microfluidic platform enabled the quantification of intrinsic bio-dielectric parameters of single cells and thus provides an effective tool in the field of flow cytometry.