Topological Deep Learning (TDL) is emerging as a promising field that leverages topological features to enhance deep learning models. This paper argues that TDL is the new frontier for relational learning, complementing graph representation learning and geometric deep learning by incorporating topological concepts. The paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations, and outlines potential solutions and future research opportunities. It highlights the importance of TDL in various machine learning settings, such as modeling multi-way interactions, capturing regularities in manifolds, and understanding topological equivariances. The paper also addresses challenges in software development, complexity, scalability, explainability, generalization, and fairness in TDL. It calls for a multidisciplinary approach and collaboration among experts in mathematics, computer science, and machine learning to advance TDL and unlock its full potential.Topological Deep Learning (TDL) is emerging as a promising field that leverages topological features to enhance deep learning models. This paper argues that TDL is the new frontier for relational learning, complementing graph representation learning and geometric deep learning by incorporating topological concepts. The paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations, and outlines potential solutions and future research opportunities. It highlights the importance of TDL in various machine learning settings, such as modeling multi-way interactions, capturing regularities in manifolds, and understanding topological equivariances. The paper also addresses challenges in software development, complexity, scalability, explainability, generalization, and fairness in TDL. It calls for a multidisciplinary approach and collaboration among experts in mathematics, computer science, and machine learning to advance TDL and unlock its full potential.