InfantCrySense utilizes an advanced audio processing pipeline combined with deep learning for automated acoustic recognition. Audio input from live streams or recordings undergoes silence trimming, RMS amplitude normalization, and fixed-length segmentation to ensure consistency across environments. The system extracts Mel-Frequency Cepstral Coefficients (MFCCs) along with temporal delta and delta-delta coefficients to construct a robust 3-channel feature matrix. This input is classified using a Convolutional Neural Network (CNN) consisting of multi-stage Conv2D blocks with batch normalization, ReLU activation, MaxPooling, Global Average Pooling, and a Softmax output layer. Trained with the Adam optimizer and Cross-Entropy Loss, the integrated framework achieves 99% precision for cry detection, delivering rapid, confidence-based real-time predictions.