Lung sound analysis plays an important role in early diagnosis of respiratory health conditions such as obstructive lung diseases, restrictive lung diseases, infectious diseases, pulmonary vascular diseases, and cancerous diseases. This study showcases an ongoing research project that investigates a method for efficiently assessing lung sounds using a combination of machine learning and signal processing. This work focuses on extracting features like time domain, frequency domain, and spectral domain from the lung sounds to improve the performance of the classification system. Time-based characteristics are measured by time domain features like signal energy, zero-crossing rate, kurtosis, and signal entropy, while the frequency content of the signal is revealed by frequency domain measures like spectral centroid, spectral bandwidth, and power spectral density. Furthermore, spectral domain data obtained using advanced signal processing techniques such as the Fourier and Wavelet transforms provides precise information about localized frequency variations across time. Using a standard dataset based on time-frequency domain features, a Random Forest classifier has provided the maximum model accuracy of 81.42% in our current study, while other models have a moderate value. This study showcases an ongoing research project that investigates a method for efficiently assessing lung sounds using a combination of machine learning and hybrid signal processing.