Waste represents a loss of valuable resources both in the form of materials and energy, many of which are scarce. Waste can be classified as—liquid waste, solid waste, organic waste, ferrous waste, hazardous waste, etc. Waste management is considered one of the key factors in the development of smart cities. With urbanization, rising income, and consumption, the production of waste has increased tremendously. It is estimated that the population of the world will be around 9.1 billion in the year 2050, out of which 68.7% will be in urban regions. Hence, there is a need for automated waste segregation for kick-starting management processes. Nowadays, three techniques have come up for waste segregation, namely—Internet of Things (IoT), Image Processing, and Deep Learning. Among these, IoT-based technique is used more than deep learning techniques or image processing techniques. The advantages of IoT-based system have been that they collect accurate data in real time, and the load cell calibration approach simplifies the calibration process. IoT-based system’s disadvantage is its expensive hardware. Deep Learning and Image Processing techniques can be used for their high accuracy and reliable results. The metallic (majorly from e-waste), plastic, and paper waste can be reused or recycled. Chemical and biological extraction techniques for metal recycling employ—hydrometallurgy, pyrometallurgy, bio-metallurgy or bioleaching, electrometallurgy, use of landfills, acid baths, and incineration. Plastics can be recycled according to types of solvent, catalyst, and product using processes namely—solvolysis, pyrolysis, and gasification. Paper recycling utilizes methods like—advanced thermal conversion methods (gasification and pyrolysis) for the conversion of secondary fiber paper mill wastes into energy vectors, microwave irradiation, and a dilute acid catalyst to hydrothermally degrade cellulose in waste paper. This study will offer input for the development of industrially relevant waste management and recycling systems.