A processor-implemented method for providing a unified platform for domain adaptable human behaviour inference, the method comprising: receiving a plurality of sensor data using a plurality of sensors, wherein the plurality of sensor data is in more than one sensor data formats; processing the received sensor data to a standard format; analyzing the processed sensor data using cross-sectional analysis to obtain low level inference to infer location, activity and physiology, wherein the cross-sectional analysis for low level inference includes analyzing location, activity, temperature and physiology; analyzing the processed sensor data using a plurality of longitudinal analysis and fourier analysis techniques based on the low level inference, domain knowledge and human metadata in a high level inference module, wherein the domain knowledge includes knowledge related to health dynamically updated in a domain database, and wherein the human metadata includes mobility, proximity, activity for a human, dynamically updated in a human metadata database; selecting a longitudinal analysis technique from the plurality of longitudinal analysis techniques for obtaining high level inference to provide insights for longitudinal tracking, prediction and anomaly detection by the high level inference module connected to the domain database and the human metadata database, wherein the longitudinal analysis technique is selected based on expectation maximization techniques, and wherein the longitudinal analysis technique includes welch analysis, cross-spectrum analysis, Feature of interest identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with the low level inference, the human metadata and the domain knowledge to provide useful and actionable insights for longitudinal tracking, prediction, and anomaly detection; and obtaining the domain adaptable human behavior inference using the unified platform based on the obtained low level inference and the high level inference to display on a unified display module based on a user query, wherein the unified platform provides a unified low level inference and high level inference of the domain adaptable human behaviour inference across multiple domains, and wherein the unified domain adaptable human behavior inference is displayed as only low level inference or only high level inference or both low level inference and the high level inference are displayed based on the user query. receiving a plurality of sensor data using a plurality of sensors, wherein the plurality of sensor data is in more than one sensor data formats; processing the received sensor data to a standard format; analyzing the processed sensor data using cross-sectional analysis to obtain low level inference to infer location, activity and physiology, wherein the cross-sectional analysis for low level inference includes analyzing location, activity, temperature and physiology; analyzing the processed sensor data using a plurality of longitudinal analysis and fourier analysis techniques based on the low level inference, domain knowledge and human metadata in a high level inference module, wherein the domain knowledge includes knowledge related to health dynamically updated in a domain database, and wherein the human metadata includes mobility, proximity, activity for a human, dynamically updated in a human metadata database; selecting a longitudinal analysis technique from the plurality of longitudinal analysis techniques for obtaining high level inference to provide insights for longitudinal tracking, prediction and anomaly detection by the high level inference module connected to the domain database and the human metadata database, wherein the longitudinal analysis technique is selected based on expectation maximization techniques, and wherein the longitudinal analysis technique includes welch analysis, cross-spectrum analysis, Feature of interest identification and time-series clustering, autocorrelation-based distance estimation and exponential smoothing, seasonal and non-seasonal models identification, ARIMA modelling, Hidden Markov models, Long short term memory (LSTM) along with the low level inference, the human metadata and the domain knowledge to provide useful and actionable insights for longitudinal tracking, prediction, and anomaly detection; and obtaining the domain adaptable human behavior inference using the unified platform based on the obtained low level inference and the high level inference to display on a unified display module based on a user query, wherein the unified platform provides a unified low level inference and high level inference of the domain adaptable human behaviour inference across multiple domains, and wherein the unified domain adaptable human behavior inference is displayed as only low level inference or only high level inference or both low level inference and the high level inference are displayed based on the user query.