The study was conducted to validate the artificial neural networks (ANN) on bamboo fiber reinforced concrete wall panel (BFRC WP) by creating a prediction model for the compressive and flexural strength of BFRCWP. Moreover, the researcher developed a BFRCWP from the database, also, evaluate its acceptability and cost analysis. Using the Artificial Neural Networks, the closest predicted value for 28 days of curing from all the data under compressive strength was 36.54 MPa with a prediction value of 36.59 MPa showing an error rate of 0.14%. For the flexural strength, the nearby predicted value was 6.851 MPa with a prediction value of 6.85 MPa having an error rate of 0.05%. The fabricated wall panel reinforced with bamboo fiber has a dimension of 62.5 mm x 585 mm x 1225 mm. The mean compressive strength of 28 days obtained from testing was 4.7 MPa and for the flexural strength was 2.53 MPa. These showed that the obtained mechanical strength from 2% bamboo fiber kawayan tinik was below the average strength expected with the mixture M20. Despite of reduction of mechanical strength of the wall panel, addition of bamboo fiber was a low-cost solution to increase the fracture resistance of the concrete mix. The cost of wall panel with bamboo fiber reinforcement was Php 207.04 cheaper than the commercially available wall panel in the market. Incorporating bamboo fiber on wall panels is recommended for animal/farm fences, outdoor household fences, and partition walls. Based on the results of the study, it can be concluded that using the Artificial Neural Networks model on predicting the mechanical properties of material was possible an« attainable, which removes the need for expensive, time-consuming, and experience personnel for manually testing.