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The protein content of feed and food ingredients has traditionally been estimated using a universal nitrogen-to-protein conversion factor (NPCF) of 6.25, assuming all proteins contain 16% nitrogen (N) and all N originates from protein. However, these assumptions are often inaccurate. Specific NPCFs have been proposed to determine the “true” protein (TP) content but are rarely implemented. This study explored the relationship between TP and N content in feed and food ingredients, proposing a predictive equation for TP based on N. Data for specific NPCFs (Ka and Kp) and N values from 55 ingredients (45 plant-based, 5 meat, 3 dairy, 1 egg, and 1 yeast product) were compiled. Simple linear regression evaluated four models: Models A and B (all 55 ingredients, TP based on Ka and Kp) and Models C and D (52 ingredients, excluding dairy, TP based on Ka and Kp). All models showed a significant linear relationship (P < 0.0001). Model C provided the best fit, with TP = 5.61 × N (R² = 0.999, Root MSE = 0.92). This study suggests that the TP of feed and food ingredients can be accurately predicted by multiplying N content by 5.61, offering a more precise approach to protein estimation.