The aqueous copolymerization of acrylamide (AM) and acrylic acid (AA) is essential for synthesizing poly(AM-co-AA), an anionic polymer widely applied in enhanced oil recovery, mining, and wastewater treatment. However, rapid reaction kinetics, high viscosity, and sampling challenges hinder real-time monitoring of monomer conversion rates using conventional offline methods. This study proposes a non-destructive in situ Raman spectroscopic approach to track conversion rates dynamically. Characteristic Raman bands associated with structural changes during polymerization are identified: C-N stretching (1386~1517 cm-1) for AM consumption, C=C stretching (1517~1732 cm-1) for SA consumption, and CH2 bending (1363 cm-1) as an invariant reference for spectral normalization. Experiments were conducted under varied initiation temperatures (1~30℃) and composite initiator ratios (azo/redox initiators: 0∶100~90∶10). Real-time Raman spectra were processed to correct baseline drift and normalized using the CH2 bending peak. Key spectral regions were analyzed to quantify peak area changes, which were correlated with monomer consumption derived from offline NMR validation. A mechanistic model linking peak area changes to conversion rates was developed, with parameters regressed via multivariate least-squares fitting. The model demonstrated high accuracy, achieving average R2 values of 0.989 and 0.981 for predicting AM and SA conversions using 1386~1517 cm-1 and 1517~1732 cm-1 bands, respectively. Independent validation tests yielded an average relative error of 2.14%. This approach enables real-time, non-invasive monitoring of copolymerization kinetics, overcoming limitations of traditional destructive techniques. By integrating spectral analysis with mechanistic modeling, the method minimizes interference from overlapping peaks and environmental fluctuations. The results underscore Raman spectroscopy's potential for online quality control in industrial batch processes, ensuring consistent product performance through precise conversion rate tracking. The established framework provides a foundation for optimizing reaction conditions and enhancing production efficiency in polymer manufacturing.