This study explores the potential of using low-computing-cost machine learning models for predicting Bending Loss in Photonic Crystal Fibers (PCFs). Algorithms for machine learning that utilise historical data and trends can be utilized to provide a potent tool for predicting Bending Loss. The bending loss data and the other associated parameters of the bent PCF were obtained using the Finite Element Method-based modal solution technique (FEM). The PCF has 3 ring air-holes in the cladding with a pitch length () of 2.6m a wavelength () of 1.55m and a silica refractive index (n) of 1.445. The bending radius was varied from of 10000m to 230m and the calculations were done in the Transverse Electric (TE) mode. The Bending Loss Dataset was used to train and evaluate five different low-computing-cost regression Algorithms such as Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, Support Vector Machine Regressor, and Gaussian Process Regression are utilized. The Linear Regression model was found to be the most accurate and reliable predictor of Bending Loss in Photonic Crystal Fibres (PCFs) achieving a Mean Square Error (MSE) of 0.0002 and an R-squared (R2) score of 0.9999. The findings of this work show how machine learning models can be used to forecast crucial PCF parameters, which could progress the field of photonics even utilizing Low-Computing-Cost computers. The use of machine learning models have the potential to greatly increase efficiency and accuracy of predicting important parameters in PCFs by improving the design and optimisation of PCFs for diverse optical applications.