The problem of developing an autonomous forklift that is able to pick-up and place pallets is not new. The same is true for pallet detection and localization, which pose interesting perception challenges due to their sparse structure. Many approaches have been presented for solving the problems of extraction, segmentation, and estimation of the pallet based on vision and Laser Rangefinder (LRF) systems. Here, the focus of attention is on the possibility of solving the problem by using a 2D LRF. On the other hand, machine learning has become a major field of research in order to handle more and more complex detection and recognition problems. The aim of this thesis is to develop a new and robust system for identifying, localizing, and tracking the pallets based on machine learning approaches, especially Convolutional Neural Network (CNN)s. The proposed system is mainly composed of two main components: Faster Region-based Convolutional Network (Faster R-CNN) detector and CNN classifier for detecting and recognizing the pallets, and a simple Kalman filter for tracking and increasing the confidence of the presence of the pallet. For fine-tuning the proposed CNNs, the system is tested systematically on real-world data containing 340 labelled object examples. Finally, performance is evaluated given the average accuracy over k-fold cross-validation. The computational complexity of the proposed system is also evaluated. Finally, the experimental results are presented, using MATLAB and ROS, verifying the feasibility and good performance of the proposed system. The best performance is achieved by our proposed CNN with an average accuracy of 99.58% for a k-fold of 10. Regarding the tacking task, the experiments are performed while the robot was moving towards the pallet. Due to availability, the experiments are carried out by considering only one pallet, and consequently to check the robustness of our algorithm, artificial data are generated by considering one more pallet in the environment. It is observed that our system is able to recognize and track the positive tracks (pallets) among other negatives tracks with high confidence scores.