Premature newborns cry to let their parents and others know what they need. It is essential to remember that premature screamers may scream for a variety of causes. Based on weeping, parents can identify a baby's emotional and physical changes and needs. However, quite challenging to pinpoint the requirement for the incubated newborns that have jaundice. To overcome these challenges, a novel deep learning-based CLJFS model is proposed for Cry-based Jaundiced Infant Signal (CLJFS) classification model. The crying is a newborn's primary communication, understanding its acoustic features can provide critical insights into the infant's condition. The proposed CLJFS model employs a multi-step process beginning with signal pre-processing using Stationary Wavelet Transform (SWT) for noise reduction and feature enhancement. Linear Prediction Coefficients (LPC) are extracted, followed by feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO). A Spiking Neural Network (SNN) then categorizes the cries into three classes: hunger, fear, and discomfort. The effectiveness of the proposed CLJFS was evaluate using F1 score, accuracy, precision, recall, and specificity. The proposed CLJFS model achieved a classification accuracy 98.9%. The proposed model enhanced the total accuracy by 2.24%, 4.03%, 10.1%, and 7.03%, respectively.