This article introduces a novel, objective, and noise-resistant algorithm for quantifying quantitative real-time polymerase chain reaction (qRT-PCR) results. The algorithm, called Real-time PCR Miner, uses mathematical models to analyze individual PCR reactions without relying on a standard curve or subjective judgments. It identifies the exponential phase (EP) of the reaction using a four-parameter logistic model and then applies a three-parameter simple exponent model for further analysis. Efficiency is calculated using an iterative nonlinear regression and weighted average, while the threshold cycle (CT) is determined by the first positive second derivative maximum from the logistic model. The algorithm is platform-independent and provides accurate, reliable results regardless of the equipment used. The method was validated using data from multiple real-time PCR systems and demonstrated consistent performance across different platforms. The algorithm outperforms traditional methods by reducing noise influence, providing more accurate efficiency estimates, and minimizing standard deviation. It is particularly useful for large sample sizes and for users who require objective, automated quantification of qRT-PCR data. The method ensures that the CT is determined within the exponential phase, which is critical for accurate quantification of mRNA levels. The algorithm is described in detail, including its application to real-world data and its advantages over existing approaches.This article introduces a novel, objective, and noise-resistant algorithm for quantifying quantitative real-time polymerase chain reaction (qRT-PCR) results. The algorithm, called Real-time PCR Miner, uses mathematical models to analyze individual PCR reactions without relying on a standard curve or subjective judgments. It identifies the exponential phase (EP) of the reaction using a four-parameter logistic model and then applies a three-parameter simple exponent model for further analysis. Efficiency is calculated using an iterative nonlinear regression and weighted average, while the threshold cycle (CT) is determined by the first positive second derivative maximum from the logistic model. The algorithm is platform-independent and provides accurate, reliable results regardless of the equipment used. The method was validated using data from multiple real-time PCR systems and demonstrated consistent performance across different platforms. The algorithm outperforms traditional methods by reducing noise influence, providing more accurate efficiency estimates, and minimizing standard deviation. It is particularly useful for large sample sizes and for users who require objective, automated quantification of qRT-PCR data. The method ensures that the CT is determined within the exponential phase, which is critical for accurate quantification of mRNA levels. The algorithm is described in detail, including its application to real-world data and its advantages over existing approaches.