Design of smart irrigation systems is a transdisciplinary work involving selecting optimal field-specific sensing devices, data capturing from these devices, their pre-processing, feature analysis, classification, IoT (Internet of Things) based actuation, and feedback operations. A wide variety of smart irrigation models, including IBM ThingSpeak, Tracxn, Farm Connect, Hydrawise, etc. have been proposed by researchers, and each of them have its own operational & deployment-specific characteristics. These models are either highly complex or have slower response time & low efficiency when applied to real-time irrigation scenarios. Moreover, these models have a higher cost, which limits their scalability levels. To overcome these issues, this text discusses design of a novel efficient IoT based irrigation platform via fuzzy bioinspired multisensory analysis of on-field parameter sets. The proposed platform uses low-cost components for sensing moisture levels, temperature levels, rain probability, NPK (Nitrogen, Phosphorous, and Potassium) levels, and soil types. These sensed values are processed via a Grey Wolf Optimizer (GWO) to identify optimum sensor types, and then a fuzzy decision layer is used for irrigation. This layer assists in the identification of efficient water flow for different crop types. The resulting growth of plants is fed-back into the model, and a Genetic Algorithm (GA) is applied to tune the fuzzy rules for better water-flow under multiple types of crops. Due to the integration of GWO with Fuzzy Logic controller, the proposed model improved yield efficiency by 8.5%, reduced computational delay by 4.9%, and reduced deployment cost compared to standard smart irrigation models. Due to these advantages, the proposed model is capable of deployment for a wide variety of real-time smart irrigation use cases.