This study focuses on the feature vector identification and remaining useful life (RUL) estimation of SAC305 solder alloy printed circuit boards (PCBs) of two different configurations during varying conditions of temperature and vibration. The feature vectors are identified using the strain signals acquired from four symmetrical locations of the PCB at regular intervals during vibration. Two different types of experiments are employed to characterize the PCB's dynamic changes with varying temperature and acceleration levels. The first type of analysis emphasizes the vibration characteristic for varying acceleration conditions while holding the temperature levels constant, and the second studies the shift in characteristics for various ambient temperatures by maintaining constant acceleration levels. The above analyses attempt to imitate an electronic board's actual working conditions in an automobile subjected to varying temperature and vibration environments. The strain signals acquired during each of these experiments are compared based on both time and frequency domain characteristics. Different statistical and frequency-based techniques were used to identify the strain signal variations with changes in the environment and loading conditions. The feature vectors in predicting failure at a constant working temperature and load were identified, and as an extension to this work, the effectiveness of the feature vectors during varying conditions of temperature and acceleration levels is investigated. The principal component analysis (PCA) is used as the data reduction and pattern recognition technique to the different operating conditions of vibration environments. The residual of the autocorrelation function (ACF) of frequency and instantaneous frequency (IF) matrices shows behavior that can predict the packages' failure on the PCB irrespective of varying conditions of temperature and acceleration levels. This feature vector's effectiveness is verified on two different configurations of PCB and for the two separate analyses discussed prior in the study. The RUL of the packages was estimated using a deep learning approach based on long short-term memory (LSTM) network. This technique can identify the underlying patterns in multivariate time series data that can predict the packages' life. The ACF's residuals were used as the multivariate time series data in conjunction with the LSTM deep learning technique to forecast the packages' life at different varying temperatures and acceleration levels during vibration.