Abstract : Construction is a key contributing sector to the economy for almost all the countries around the world and in Kingdom of Bahrain particularly. This sector is booming as a result of Gulf Cooperation Council development funded projects and the government directions towards economic diversification, which provides abundant opportunities for non-oil sectors such as the construction sector. Construction however, is labor intensive and affected by various factors resulting into labor productivity variations; therefore the aim of this research is to identify and assess the productivity influencing factors for key construction activities (namely: steel fixing, concrete pouring, formwork, and block work). To achieve this objective, a concise literature review was conducted about construction labor productivity and significant factors influencing it. The conclusions drawn in addition to industry's experts' opinion were utilized to develop a questionnaire to identify the most influencing factors in Bahrain. The targeted population were grades A and AA contractors. Conversely historical data for construction projects were collected to be used in Construction Labor Productivity (CLP) modeling along with the outputs of the questionnaire. The productivity models for the selected activities were developed using artificial neural network (ANN) technique (in NeuroXL) and three types of regression analysis which are linear, stepwise, and nonlinear regression (in Minitab). These models quantified the effect of various factors on the productivity and enabled predicting it with new data. The goodness of fit and prediction performance of the models were assessed based on the error measures: Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Deviation (MAD), and Mean Absolute Percentage Error (MAPE) in addition to Coefficient of Determination (R2). The performance of neural network models was better in predicting CLP for steel fixing, formwork, and blockwork, however, for concrete pouring the linear regression was more suitable. The study emphasized on the importance of CLP data records keeping for future analysis, and the effectiveness of ANN in modeling CLP, and suggested potential improvement to CLP through dealing effectively with the identified significant influencing factors.