Carbon (C), one of the most abundant elements in nature, displays various types of hybridized bonds, contributing to a rich energy landscape and a diverse range of properties across different structural phases. This inherent versatility, combined with boundless possibilities of its combination with hydrogen (H) leads to a plethora of structures and chemical environments, ranging from simple hydrocarbons like CH 4 to complex organic molecules like carotenes (C 40 H 56 ). The investigation of the hydrocarbons and other C-H systems at atomistic level is crucial for understanding chemical interactions and advancing materials design, thereby, attracting significant attention from researchers. For instance, the advancements in C-based materials have revolutionized fields like hydrogen storage and the capture of polycyclic aromatic hydrocarbons pollutants. Recent advancements in theoretical and computational methodologies, particularly those based on quantum mechanics (QM), such as density functional theory (DFT) have significantly enhanced our ability to study and explore materials at the atomic scale. While QM methods provide accurate understanding into material behavior, the computational cost of them increases with the system size, hindering their applications for exploring extensive energy landscapes or large-scale simulations. Therefore, we need a trade-off between accuracy and computational cost in modeling materials at atomic scale. Machine learning interatomic potentials (MLIPs), as computationally efficient alternatives to QM-based methods, have gained attention for their ability to capture complex atomic interactions and predict material properties with remarkable precision, enabling the exploration of extensive chemical spaces and previously inaccessible molecular dynamics (MD). There have been numerous efforts to develop MLIPs specifically for pure C. These studies aim to improve the accuracy and transferability of the potential by training on dataset covering a broad spectrum of atomic environments and configurations, such as MD trajectories at different temperatures and pressures or including 0D-3D systems to have diverse boundary conditions to capture the bond diversity. Based on specific applications, ongoing efforts aim to improve MLIPs by presenting different versions. For instance, the Gaussian approximation potential (GAP) was first developed to study the behavior of liquid and amorphous C, later improved to encompass van der Waals corrections for C 60 fullerene and nanoporous C structures, and later ordered graphite configurations with different stacking patterns were added to its training dataset for exploring the graphitic energy landscape of C. The existence of MLIPs specifically tailored for pure C highlights the difficulty and challenges in modeling such systems. Pure C itself presents significant training challenges; incorporating H to develop accurate MLIPs for C-H systems adds further complexity. This includes effectively capturing bond variations and intramolecular interactions, such as hydrogen bonding. Achieving transferability of the MLIP across systems with various C/H ratios and different boundary conditions is more demanding than for pure C systems. This is due to the greater complexity and diversity of C-H compounds, necessitating the generation of a larger and more diverse training dataset. Efforts have been made to train MLIPs that include C and H for specific applications, such as predicting CH stretching modes in small molecules, C-C bond breaking in small molecules, C-H bond activation of CH 4 on Pt(111), and constructing the potential energy surface (PES) of CH 4 and study its vibrational levels. Some other efforts have aimed to go beyond specific system and provide a general descriptions for all organic molecules, rather than for specific system. However, these methods lack sufficient accuracy for different systems that are not essentially close to their equilibrium state. In this study, we present a MLIP based on artificial neural networks (ANNs) for C-H systems. To enhance the diversity of the training dataset to represent the complexities of the systems, we train the ANN potential on cluster systems, rather than including different boundary conditions. This approach provides a broader representation of the system's behavior. We demonstrate that our trained potential, solely based on cluster C-H systems, can be applied not only to C-H systems but also to pure C systems under various boundary conditions. Furthermore, our potential's accuracy and versatility enable it to be used in diverse contexts, including reactivity and lattice dynamics. The trained ANN potential is also utilized for crystal structure prediction (CSP) and has identified a novel C polymorph, showcasing the potential's practical applications.