This paper presents a deep learning (DL) framework for predicting biodiesel production from coconut oil, aiming to contribute towards sustainable and renewable energy solutions. The proposed framework utilizes Faster R-CNN with a ResNet-50 backbone and YOLOv8 for accurate coconut detection and identification. By employing the power of DL, the framework enables prediction of matured coconut fruit oil, serving as a crucial initial phase in biodiesel production. This research serves as a valuable contribution to the renewable energy sector and encourages further exploration and development of DL techniques in biodiesel production. The framework comprises of three phases: in the first phase, a dataset of 908 images of matured coconuts is collected and pre-processed. In the second phase, Faster R-CNN with a ResNet-50 and YOLOv8 models are deployed for coconut detection. The third phase involves using the detected number of coconuts to predict the yield oil. To validate the performance of the proposed framework, several variants of Faster R-CNN as well as YOLOv8 models are deployed. The experimental results demonstrate that YOLOv8 outperforms other models. The proposed framework offers a significant advancement in the field by providing the interested community with a reliable tool for predicting biodiesel energy from coconut oil. The findings illustrate the success of the proposed framework, paving the way for enhanced and sustainable biodiesel production.