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Machine consciousness (MC) is the ultimate challenge to artificial intelligence. Although great progress has been made in artificial intelligence and robotics, consciousness is still an enigma and machines are far from having it. To clarify the concepts of consciousness and the research directions of machine consciousness, in this review, a comprehensive taxonomy for machine consciousness is proposed, categorizing it into seven types: MC-Perception, MC-Cognition, MC-Behavior, MC-Mechanism, MC-Self, MC-Qualia and MC-Test, where the first six types aim to achieve a certain kind of conscious ability, and the last type aims to provide evaluation methods and criteria for machine consciousness. For each type, the specific research contents and future developments are discussed in detail. Especially, the machine implementations of three influential consciousness theories, i.e. global workspace theory, integrated information theory and higher-order theory, are elaborated in depth. Moreover, the challenges and outlook of machine consciousness are analyzed in detail from both theoretical and technical perspectives, with emphasis on new methods and technologies that have the potential to realize machine consciousness, such as brain-inspired computing, quantum computing and hybrid intelligence. The ethical implications of machine consciousness are also discussed. Finally, a comprehensive implementation framework of machine consciousness is provided, integrating five suggested research perspectives: consciousness theories, computational methods, cognitive architectures, experimental systems, and test platforms, paving the way for the future developments of machine consciousness.